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marcelomoreno26
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all-MiniLM-L6-v2-absa-aspect

Text Classification
setfit
Safetensors
sentence-transformers
bert
absa
generated_from_setfit_trainer
Eval Results
Model card Files Community
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Add SetFit ABSA model

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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +1486 -0
  3. config.json +26 -0
  4. config_sentence_transformers.json +9 -0
  5. config_setfit.json +9 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +20 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +37 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +64 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 384,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ library_name: setfit
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+ tags:
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+ - setfit
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+ - absa
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ base_model: sentence-transformers/all-MiniLM-L6-v2
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+ metrics:
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+ - accuracy
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+ widget:
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+ - text: netbook:I am not going to sit here and complain about it not having a cd drive
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+ and what not because it is a netbook, it is made to be compact and if you want
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+ all the other stuff get a laptop.
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+ - text: price:I finally decided on this laptop because it was the right price for
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+ what I need it.
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+ - text: shipped:This laptop looked brand new and was shipped very quickly.
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+ - text: business:They offer the best warranty in the business, and don't 3rd party
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+ it out like Toshiba.
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+ - text: email:My husband uses it mostly for games, email and music.
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+ pipeline_tag: text-classification
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+ inference: false
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+ model-index:
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+ - name: SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.8947936336660373
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+ name: Accuracy
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+ ---
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+
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+ # SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. In particular, this model is in charge of filtering aspect span candidates.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ This model was trained within the context of a larger system for ABSA, which looks like so:
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+
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+ 1. Use a spaCy model to select possible aspect span candidates.
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+ 2. **Use this SetFit model to filter these possible aspect span candidates.**
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+ 3. Use a SetFit model to classify the filtered aspect span candidates.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **spaCy Model:** en_core_web_sm
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+ - **SetFitABSA Aspect Model:** [marcelomoreno26/all-MiniLM-L6-v2-absa-aspect2](https://huggingface.co/marcelomoreno26/all-MiniLM-L6-v2-absa-aspect2)
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+ - **SetFitABSA Polarity Model:** [setfit-absa-polarity](https://huggingface.co/setfit-absa-polarity)
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+ - **Maximum Sequence Length:** 256 tokens
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+ - **Number of Classes:** 2 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:----------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | aspect | <ul><li>'cord:I charge it at night and skip taking the cord with me because of the good battery life.'</li><li>'battery life:I charge it at night and skip taking the cord with me because of the good battery life.'</li><li>'service center:The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the "sales" team, which is the retail shop which I bought my netbook from.'</li></ul> |
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+ | no aspect | <ul><li>'night:I charge it at night and skip taking the cord with me because of the good battery life.'</li><li>'skip:I charge it at night and skip taking the cord with me because of the good battery life.'</li><li>'exchange:The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the "sales" team, which is the retail shop which I bought my netbook from.'</li></ul> |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.8948 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import AbsaModel
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+
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+ # Download from the 🤗 Hub
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+ model = AbsaModel.from_pretrained(
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+ "marcelomoreno26/all-MiniLM-L6-v2-absa-aspect2",
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+ "setfit-absa-polarity",
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+ )
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+ # Run inference
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+ preds = model("The food was great, but the venue is just way too busy.")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:--------|:----|
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+ | Word count | 2 | 21.9670 | 75 |
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+
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+ | Label | Training Sample Count |
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+ |:----------|:----------------------|
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+ | no aspect | 690 |
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+ | aspect | 644 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 2)
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+ - num_epochs: (1, 16)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - body_learning_rate: (2e-05, 1e-05)
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+ - head_learning_rate: 0.01
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: False
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+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: False
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:------:|:-----:|:-------------:|:---------------:|
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+ | 0.0000 | 1 | 0.3662 | - |
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+ | 0.0015 | 50 | 0.3374 | - |
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+ | 0.0029 | 100 | 0.3411 | - |
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+ | 0.0044 | 150 | 0.2945 | - |
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+ | 0.0059 | 200 | 0.2944 | - |
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+ | 0.0073 | 250 | 0.2942 | - |
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+ | 0.0088 | 300 | 0.2409 | - |
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+ | 0.0103 | 350 | 0.2817 | - |
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+ | 0.0118 | 400 | 0.3149 | - |
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+ | 0.0132 | 450 | 0.2618 | - |
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+ | 0.0147 | 500 | 0.247 | - |
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+ | 0.0162 | 550 | 0.2883 | - |
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+ | 0.0176 | 600 | 0.2783 | - |
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+ | 0.0191 | 650 | 0.2418 | - |
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+ | 0.0206 | 700 | 0.2938 | - |
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+ | 0.0220 | 750 | 0.2376 | - |
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+ | 0.0235 | 800 | 0.2652 | - |
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+ | 0.0250 | 850 | 0.2442 | - |
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+ | 0.0265 | 900 | 0.2678 | - |
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+ | 0.0279 | 950 | 0.2216 | - |
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+ | 0.0294 | 1000 | 0.1816 | - |
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+ | 0.0309 | 1050 | 0.1102 | - |
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+ | 0.0323 | 1100 | 0.2985 | - |
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+ | 0.0338 | 1150 | 0.1124 | - |
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+ | 0.0353 | 1200 | 0.1075 | - |
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+ | 0.0367 | 1250 | 0.0819 | - |
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+ | 0.0382 | 1300 | 0.1238 | - |
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+ | 0.0397 | 1350 | 0.0529 | - |
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+ | 0.0412 | 1400 | 0.026 | - |
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+ | 0.0426 | 1450 | 0.0289 | - |
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+ | 0.0441 | 1500 | 0.067 | - |
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+ | 0.0456 | 1550 | 0.0276 | - |
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+ | 0.0470 | 1600 | 0.0162 | - |
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+ | 0.0485 | 1650 | 0.0083 | - |
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+ | 0.0500 | 1700 | 0.0017 | - |
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+ | 0.0514 | 1750 | 0.0028 | - |
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+ | 0.0529 | 1800 | 0.0045 | - |
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+ | 0.0544 | 1850 | 0.0022 | - |
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+ | 0.0558 | 1900 | 0.0014 | - |
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+ | 0.0573 | 1950 | 0.0059 | - |
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+ | 0.0588 | 2000 | 0.0019 | - |
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+ | 0.0603 | 2050 | 0.0014 | - |
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+ | 0.0617 | 2100 | 0.0022 | - |
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+ | 0.0632 | 2150 | 0.0005 | - |
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+ | 0.0647 | 2200 | 0.0008 | - |
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+ | 0.0661 | 2250 | 0.0005 | - |
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+ | 0.0676 | 2300 | 0.0006 | - |
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+ | 0.0691 | 2350 | 0.0003 | - |
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+ | 0.0705 | 2400 | 0.0007 | - |
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+ | 0.0720 | 2450 | 0.0005 | - |
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+ | 0.0735 | 2500 | 0.0005 | - |
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+ | 0.0750 | 2550 | 0.0612 | - |
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+ | 0.0764 | 2600 | 0.0004 | - |
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+ | 0.0779 | 2650 | 0.041 | - |
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+ | 0.0794 | 2700 | 0.0002 | - |
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+ | 0.0808 | 2750 | 0.0003 | - |
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+ | 0.0823 | 2800 | 0.0002 | - |
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+ | 0.0838 | 2850 | 0.0002 | - |
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+ | 0.0852 | 2900 | 0.0002 | - |
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+ | 0.0867 | 2950 | 0.0004 | - |
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+ | 0.0882 | 3000 | 0.0006 | - |
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+ | 0.0897 | 3050 | 0.0601 | - |
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+ | 0.0911 | 3100 | 0.0002 | - |
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+ | 0.0926 | 3150 | 0.0108 | - |
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+ | 0.0941 | 3200 | 0.0003 | - |
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+ | 0.0955 | 3250 | 0.0363 | - |
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+ | 0.0970 | 3300 | 0.0006 | - |
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+ | 0.0985 | 3350 | 0.0002 | - |
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+ | 0.0999 | 3400 | 0.0033 | - |
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+ | 0.1029 | 3500 | 0.0002 | - |
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+ | 0.1044 | 3550 | 0.0006 | - |
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+ | 0.1058 | 3600 | 0.0002 | - |
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+ | 0.1088 | 3700 | 0.0001 | - |
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+ | 0.1102 | 3750 | 0.0002 | - |
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+ | 0.1117 | 3800 | 0.0002 | - |
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+ | 0.1132 | 3850 | 0.0004 | - |
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+ | 0.1146 | 3900 | 0.0003 | - |
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+ | 0.1161 | 3950 | 0.0001 | - |
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+ | 0.1176 | 4000 | 0.0004 | - |
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+ | 0.1190 | 4050 | 0.0003 | - |
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+ | 0.1205 | 4100 | 0.001 | - |
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+ | 0.1220 | 4150 | 0.0002 | - |
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+ | 0.1235 | 4200 | 0.0001 | - |
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+ | 0.1249 | 4250 | 0.0003 | - |
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+ | 0.1264 | 4300 | 0.0003 | - |
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+ | 0.1279 | 4350 | 0.0002 | - |
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+ | 0.1293 | 4400 | 0.0001 | - |
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+ | 0.1308 | 4450 | 0.0001 | - |
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+ | 0.1323 | 4500 | 0.0001 | - |
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+ | 0.1337 | 4550 | 0.0001 | - |
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+ | 0.1352 | 4600 | 0.0001 | - |
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+ | 0.1367 | 4650 | 0.0003 | - |
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+ | 0.1382 | 4700 | 0.0006 | - |
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+ | 0.1396 | 4750 | 0.0003 | - |
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+ | 0.1411 | 4800 | 0.0001 | - |
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+ | 0.1426 | 4850 | 0.0011 | - |
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+ | 0.1440 | 4900 | 0.0001 | - |
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+ | 0.1455 | 4950 | 0.0001 | - |
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+ | 0.1470 | 5000 | 0.0001 | - |
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+ | 0.1484 | 5050 | 0.0001 | - |
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+ | 0.1499 | 5100 | 0.0002 | - |
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+ | 0.1514 | 5150 | 0.0497 | - |
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+ | 0.1529 | 5200 | 0.0002 | - |
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+ | 0.1543 | 5250 | 0.0001 | - |
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+ | 0.1558 | 5300 | 0.0008 | - |
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+ | 0.1573 | 5350 | 0.0001 | - |
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+ | 0.1587 | 5400 | 0.0002 | - |
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+ | 0.1602 | 5450 | 0.0001 | - |
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+ | 0.1617 | 5500 | 0.0003 | - |
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+ | 0.1631 | 5550 | 0.0003 | - |
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+ | 0.1646 | 5600 | 0.0004 | - |
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+ | 0.1661 | 5650 | 0.0002 | - |
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+ | 0.1675 | 5700 | 0.0002 | - |
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+ | 0.1690 | 5750 | 0.0001 | - |
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+ | 0.1705 | 5800 | 0.0001 | - |
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+ | 0.1720 | 5850 | 0.0001 | - |
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+ | 0.1734 | 5900 | 0.0004 | - |
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+ | 0.1749 | 5950 | 0.0001 | - |
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+ | 0.1764 | 6000 | 0.0001 | - |
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+ | 0.1778 | 6050 | 0.0001 | - |
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+ | 0.125 | 1 | 0.0002 | - |
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+ | 0.5 | 4 | 0.0003 | - |
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+ | 1.0 | 8 | 0.0 | - |
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+ | 0.0000 | 1 | 0.0001 | - |
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+ | 0.0015 | 50 | 0.0001 | - |
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+ | 0.0029 | 100 | 0.0 | - |
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+ | 0.0044 | 150 | 0.0001 | - |
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+ | 0.125 | 1 | 0.0 | - |
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+ | 0.5 | 4 | 0.0 | - |
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+ | 0.0000 | 1 | 0.0003 | - |
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+ | 0.0009 | 50 | 0.0003 | - |
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+ | 0.0018 | 100 | 0.0003 | - |
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+ | 0.0027 | 150 | 0.0001 | - |
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+ | 0.0036 | 200 | 0.0001 | - |
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+ | 0.0045 | 250 | 0.1015 | - |
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+ | 0.0054 | 300 | 0.0005 | - |
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+ | 0.0063 | 350 | 0.0579 | - |
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+ | 0.0072 | 400 | 0.0001 | - |
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+ | 0.0081 | 450 | 0.0897 | - |
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+ | 0.0090 | 500 | 0.0618 | - |
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+ | 0.0099 | 550 | 0.0002 | - |
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+ | 0.0108 | 600 | 0.0001 | - |
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+ | 0.0117 | 650 | 0.0004 | - |
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+ | 0.0126 | 700 | 0.0002 | - |
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+ | 0.0135 | 750 | 0.0002 | - |
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+ | 0.0143 | 800 | 0.0001 | - |
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+ | 0.0152 | 850 | 0.062 | - |
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+ | 0.0161 | 900 | 0.0004 | - |
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+ | 0.0170 | 950 | 0.0002 | - |
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+ | 0.0179 | 1000 | 0.0001 | - |
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+ | 0.0188 | 1050 | 0.0628 | - |
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+ | 0.0197 | 1100 | 0.0003 | - |
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+ | 0.0206 | 1150 | 0.0003 | - |
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+ | 0.0215 | 1200 | 0.0001 | - |
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+ | 0.0224 | 1250 | 0.0001 | - |
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+ | 0.0233 | 1300 | 0.0001 | - |
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+ | 0.0000 | 1 | 0.0002 | - |
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+ | 0.0009 | 50 | 0.0002 | - |
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+ | 0.0018 | 100 | 0.0001 | - |
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+ | 0.0027 | 150 | 0.0001 | - |
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+ | 0.0036 | 200 | 0.0001 | - |
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+ | 0.0045 | 250 | 0.0002 | - |
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+ | 0.0054 | 300 | 0.0001 | - |
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+ | 0.0063 | 350 | 0.0002 | - |
335
+ | 0.0072 | 400 | 0.0002 | - |
336
+ | 0.0081 | 450 | 0.0262 | - |
337
+ | 0.0090 | 500 | 0.0001 | - |
338
+ | 0.0099 | 550 | 0.0002 | - |
339
+ | 0.0108 | 600 | 0.0001 | - |
340
+ | 0.0117 | 650 | 0.0001 | - |
341
+ | 0.0126 | 700 | 0.0001 | - |
342
+ | 0.0135 | 750 | 0.0001 | - |
343
+ | 0.0143 | 800 | 0.0001 | - |
344
+ | 0.0152 | 850 | 0.0002 | - |
345
+ | 0.0161 | 900 | 0.0001 | - |
346
+ | 0.0170 | 950 | 0.0001 | - |
347
+ | 0.0179 | 1000 | 0.0001 | - |
348
+ | 0.0188 | 1050 | 0.06 | - |
349
+ | 0.0197 | 1100 | 0.0001 | - |
350
+ | 0.0206 | 1150 | 0.0001 | - |
351
+ | 0.0215 | 1200 | 0.0001 | - |
352
+ | 0.0224 | 1250 | 0.0001 | - |
353
+ | 0.0233 | 1300 | 0.0001 | - |
354
+ | 0.0242 | 1350 | 0.0001 | - |
355
+ | 0.0251 | 1400 | 0.0001 | - |
356
+ | 0.0260 | 1450 | 0.0001 | - |
357
+ | 0.0269 | 1500 | 0.0002 | - |
358
+ | 0.0278 | 1550 | 0.0001 | - |
359
+ | 0.0287 | 1600 | 0.0001 | - |
360
+ | 0.0296 | 1650 | 0.0125 | - |
361
+ | 0.0305 | 1700 | 0.0001 | - |
362
+ | 0.0314 | 1750 | 0.0001 | - |
363
+ | 0.0323 | 1800 | 0.0001 | - |
364
+ | 0.0332 | 1850 | 0.0001 | - |
365
+ | 0.0341 | 1900 | 0.0001 | - |
366
+ | 0.0350 | 1950 | 0.0001 | - |
367
+ | 0.0359 | 2000 | 0.0001 | - |
368
+ | 0.0368 | 2050 | 0.0001 | - |
369
+ | 0.0377 | 2100 | 0.0002 | - |
370
+ | 0.0386 | 2150 | 0.0001 | - |
371
+ | 0.0395 | 2200 | 0.0001 | - |
372
+ | 0.0404 | 2250 | 0.0407 | - |
373
+ | 0.0412 | 2300 | 0.0001 | - |
374
+ | 0.0421 | 2350 | 0.0001 | - |
375
+ | 0.0430 | 2400 | 0.0001 | - |
376
+ | 0.0439 | 2450 | 0.0001 | - |
377
+ | 0.0448 | 2500 | 0.0001 | - |
378
+ | 0.0457 | 2550 | 0.0 | - |
379
+ | 0.0466 | 2600 | 0.0 | - |
380
+ | 0.0475 | 2650 | 0.0001 | - |
381
+ | 0.0484 | 2700 | 0.0 | - |
382
+ | 0.0493 | 2750 | 0.0001 | - |
383
+ | 0.0502 | 2800 | 0.0001 | - |
384
+ | 0.0511 | 2850 | 0.0001 | - |
385
+ | 0.0520 | 2900 | 0.0001 | - |
386
+ | 0.0529 | 2950 | 0.0002 | - |
387
+ | 0.0538 | 3000 | 0.0001 | - |
388
+ | 0.0547 | 3050 | 0.0001 | - |
389
+ | 0.0556 | 3100 | 0.0001 | - |
390
+ | 0.0565 | 3150 | 0.0001 | - |
391
+ | 0.0574 | 3200 | 0.0 | - |
392
+ | 0.0583 | 3250 | 0.0 | - |
393
+ | 0.0592 | 3300 | 0.0 | - |
394
+ | 0.0601 | 3350 | 0.0001 | - |
395
+ | 0.0610 | 3400 | 0.0 | - |
396
+ | 0.0619 | 3450 | 0.0 | - |
397
+ | 0.0628 | 3500 | 0.0001 | - |
398
+ | 0.0637 | 3550 | 0.0001 | - |
399
+ | 0.0646 | 3600 | 0.0 | - |
400
+ | 0.0655 | 3650 | 0.0001 | - |
401
+ | 0.0664 | 3700 | 0.0 | - |
402
+ | 0.0673 | 3750 | 0.0001 | - |
403
+ | 0.0681 | 3800 | 0.0 | - |
404
+ | 0.0690 | 3850 | 0.0005 | - |
405
+ | 0.0699 | 3900 | 0.0001 | - |
406
+ | 0.0708 | 3950 | 0.0001 | - |
407
+ | 0.0717 | 4000 | 0.0 | - |
408
+ | 0.0726 | 4050 | 0.0001 | - |
409
+ | 0.0735 | 4100 | 0.0009 | - |
410
+ | 0.0744 | 4150 | 0.0001 | - |
411
+ | 0.0753 | 4200 | 0.0001 | - |
412
+ | 0.0762 | 4250 | 0.0001 | - |
413
+ | 0.0771 | 4300 | 0.0 | - |
414
+ | 0.0780 | 4350 | 0.0001 | - |
415
+ | 0.0789 | 4400 | 0.0001 | - |
416
+ | 0.0798 | 4450 | 0.0001 | - |
417
+ | 0.0807 | 4500 | 0.0 | - |
418
+ | 0.0816 | 4550 | 0.0 | - |
419
+ | 0.0825 | 4600 | 0.0001 | - |
420
+ | 0.0834 | 4650 | 0.0 | - |
421
+ | 0.0843 | 4700 | 0.0 | - |
422
+ | 0.0852 | 4750 | 0.0 | - |
423
+ | 0.0861 | 4800 | 0.0 | - |
424
+ | 0.0870 | 4850 | 0.0 | - |
425
+ | 0.0879 | 4900 | 0.0004 | - |
426
+ | 0.0888 | 4950 | 0.0002 | - |
427
+ | 0.0897 | 5000 | 0.0001 | - |
428
+ | 0.0906 | 5050 | 0.0001 | - |
429
+ | 0.0915 | 5100 | 0.0 | - |
430
+ | 0.0924 | 5150 | 0.0026 | - |
431
+ | 0.0933 | 5200 | 0.0549 | - |
432
+ | 0.0942 | 5250 | 0.0001 | - |
433
+ | 0.0950 | 5300 | 0.0011 | - |
434
+ | 0.0959 | 5350 | 0.0 | - |
435
+ | 0.0968 | 5400 | 0.0 | - |
436
+ | 0.0977 | 5450 | 0.0 | - |
437
+ | 0.0986 | 5500 | 0.0002 | - |
438
+ | 0.0995 | 5550 | 0.0001 | - |
439
+ | 0.1004 | 5600 | 0.0 | - |
440
+ | 0.1013 | 5650 | 0.0001 | - |
441
+ | 0.1022 | 5700 | 0.0001 | - |
442
+ | 0.1031 | 5750 | 0.0 | - |
443
+ | 0.1040 | 5800 | 0.0 | - |
444
+ | 0.1049 | 5850 | 0.0 | - |
445
+ | 0.1058 | 5900 | 0.0203 | - |
446
+ | 0.1067 | 5950 | 0.0001 | - |
447
+ | 0.1076 | 6000 | 0.0 | - |
448
+ | 0.1085 | 6050 | 0.0 | - |
449
+ | 0.1094 | 6100 | 0.0 | - |
450
+ | 0.1103 | 6150 | 0.0 | - |
451
+ | 0.1112 | 6200 | 0.0001 | - |
452
+ | 0.1121 | 6250 | 0.0 | - |
453
+ | 0.1130 | 6300 | 0.0 | - |
454
+ | 0.1139 | 6350 | 0.0 | - |
455
+ | 0.1148 | 6400 | 0.0 | - |
456
+ | 0.1157 | 6450 | 0.0164 | - |
457
+ | 0.1166 | 6500 | 0.0001 | - |
458
+ | 0.1175 | 6550 | 0.0 | - |
459
+ | 0.1184 | 6600 | 0.0001 | - |
460
+ | 0.1193 | 6650 | 0.0002 | - |
461
+ | 0.1202 | 6700 | 0.0001 | - |
462
+ | 0.1211 | 6750 | 0.0 | - |
463
+ | 0.1219 | 6800 | 0.0 | - |
464
+ | 0.1228 | 6850 | 0.0 | - |
465
+ | 0.1237 | 6900 | 0.0 | - |
466
+ | 0.1246 | 6950 | 0.0 | - |
467
+ | 0.1255 | 7000 | 0.0001 | - |
468
+ | 0.1264 | 7050 | 0.0 | - |
469
+ | 0.1273 | 7100 | 0.0 | - |
470
+ | 0.1282 | 7150 | 0.0 | - |
471
+ | 0.1291 | 7200 | 0.0002 | - |
472
+ | 0.1300 | 7250 | 0.0 | - |
473
+ | 0.1309 | 7300 | 0.0 | - |
474
+ | 0.1318 | 7350 | 0.0 | - |
475
+ | 0.1327 | 7400 | 0.0 | - |
476
+ | 0.1336 | 7450 | 0.0 | - |
477
+ | 0.1345 | 7500 | 0.0002 | - |
478
+ | 0.1354 | 7550 | 0.0 | - |
479
+ | 0.1363 | 7600 | 0.0 | - |
480
+ | 0.1372 | 7650 | 0.0001 | - |
481
+ | 0.1381 | 7700 | 0.0001 | - |
482
+ | 0.1390 | 7750 | 0.0001 | - |
483
+ | 0.1399 | 7800 | 0.0001 | - |
484
+ | 0.1408 | 7850 | 0.0 | - |
485
+ | 0.1417 | 7900 | 0.0 | - |
486
+ | 0.1426 | 7950 | 0.0 | - |
487
+ | 0.1435 | 8000 | 0.0142 | - |
488
+ | 0.1444 | 8050 | 0.0001 | - |
489
+ | 0.1453 | 8100 | 0.0 | - |
490
+ | 0.1462 | 8150 | 0.0002 | - |
491
+ | 0.1471 | 8200 | 0.0 | - |
492
+ | 0.1480 | 8250 | 0.0 | - |
493
+ | 0.1488 | 8300 | 0.0 | - |
494
+ | 0.1497 | 8350 | 0.0 | - |
495
+ | 0.1506 | 8400 | 0.0003 | - |
496
+ | 0.1515 | 8450 | 0.0 | - |
497
+ | 0.1524 | 8500 | 0.0 | - |
498
+ | 0.1533 | 8550 | 0.0 | - |
499
+ | 0.1542 | 8600 | 0.0 | - |
500
+ | 0.1551 | 8650 | 0.0 | - |
501
+ | 0.1560 | 8700 | 0.0 | - |
502
+ | 0.1569 | 8750 | 0.0 | - |
503
+ | 0.1578 | 8800 | 0.0 | - |
504
+ | 0.1587 | 8850 | 0.0 | - |
505
+ | 0.1596 | 8900 | 0.0 | - |
506
+ | 0.1605 | 8950 | 0.0 | - |
507
+ | 0.1614 | 9000 | 0.0 | - |
508
+ | 0.1623 | 9050 | 0.0 | - |
509
+ | 0.1632 | 9100 | 0.0 | - |
510
+ | 0.1641 | 9150 | 0.0 | - |
511
+ | 0.1650 | 9200 | 0.0 | - |
512
+ | 0.1659 | 9250 | 0.0001 | - |
513
+ | 0.1668 | 9300 | 0.0 | - |
514
+ | 0.1677 | 9350 | 0.0 | - |
515
+ | 0.1686 | 9400 | 0.0 | - |
516
+ | 0.1695 | 9450 | 0.0 | - |
517
+ | 0.1704 | 9500 | 0.0 | - |
518
+ | 0.1713 | 9550 | 0.0 | - |
519
+ | 0.1722 | 9600 | 0.0 | - |
520
+ | 0.1731 | 9650 | 0.0 | - |
521
+ | 0.1740 | 9700 | 0.0 | - |
522
+ | 0.1749 | 9750 | 0.0 | - |
523
+ | 0.1758 | 9800 | 0.0 | - |
524
+ | 0.1766 | 9850 | 0.0 | - |
525
+ | 0.1775 | 9900 | 0.0 | - |
526
+ | 0.1784 | 9950 | 0.0 | - |
527
+ | 0.1793 | 10000 | 0.0 | - |
528
+ | 0.1802 | 10050 | 0.0097 | - |
529
+ | 0.1811 | 10100 | 0.0 | - |
530
+ | 0.1820 | 10150 | 0.0 | - |
531
+ | 0.1829 | 10200 | 0.0 | - |
532
+ | 0.1838 | 10250 | 0.0 | - |
533
+ | 0.1847 | 10300 | 0.0001 | - |
534
+ | 0.1856 | 10350 | 0.0 | - |
535
+ | 0.1865 | 10400 | 0.0 | - |
536
+ | 0.1874 | 10450 | 0.0 | - |
537
+ | 0.1883 | 10500 | 0.0 | - |
538
+ | 0.1892 | 10550 | 0.0 | - |
539
+ | 0.1901 | 10600 | 0.0 | - |
540
+ | 0.1910 | 10650 | 0.0 | - |
541
+ | 0.1919 | 10700 | 0.0 | - |
542
+ | 0.1928 | 10750 | 0.0 | - |
543
+ | 0.1937 | 10800 | 0.0 | - |
544
+ | 0.1946 | 10850 | 0.0 | - |
545
+ | 0.1955 | 10900 | 0.0 | - |
546
+ | 0.1964 | 10950 | 0.0 | - |
547
+ | 0.1973 | 11000 | 0.0001 | - |
548
+ | 0.1982 | 11050 | 0.0 | - |
549
+ | 0.1991 | 11100 | 0.0 | - |
550
+ | 0.2000 | 11150 | 0.0 | - |
551
+ | 0.2009 | 11200 | 0.0 | - |
552
+ | 0.2018 | 11250 | 0.0004 | - |
553
+ | 0.2027 | 11300 | 0.0001 | - |
554
+ | 0.2035 | 11350 | 0.0001 | - |
555
+ | 0.2044 | 11400 | 0.0 | - |
556
+ | 0.2053 | 11450 | 0.0001 | - |
557
+ | 0.2062 | 11500 | 0.0 | - |
558
+ | 0.2071 | 11550 | 0.0001 | - |
559
+ | 0.2080 | 11600 | 0.0 | - |
560
+ | 0.2089 | 11650 | 0.0 | - |
561
+ | 0.2098 | 11700 | 0.0 | - |
562
+ | 0.2107 | 11750 | 0.0 | - |
563
+ | 0.2116 | 11800 | 0.0 | - |
564
+ | 0.2125 | 11850 | 0.0 | - |
565
+ | 0.2134 | 11900 | 0.0 | - |
566
+ | 0.2143 | 11950 | 0.0001 | - |
567
+ | 0.2152 | 12000 | 0.0 | - |
568
+ | 0.2161 | 12050 | 0.0 | - |
569
+ | 0.2170 | 12100 | 0.0 | - |
570
+ | 0.2179 | 12150 | 0.0 | - |
571
+ | 0.2188 | 12200 | 0.0 | - |
572
+ | 0.2197 | 12250 | 0.0 | - |
573
+ | 0.2206 | 12300 | 0.0 | - |
574
+ | 0.2215 | 12350 | 0.0 | - |
575
+ | 0.2224 | 12400 | 0.0 | - |
576
+ | 0.2233 | 12450 | 0.0 | - |
577
+ | 0.2242 | 12500 | 0.0 | - |
578
+ | 0.2251 | 12550 | 0.0 | - |
579
+ | 0.2260 | 12600 | 0.0 | - |
580
+ | 0.2269 | 12650 | 0.0 | - |
581
+ | 0.2278 | 12700 | 0.0 | - |
582
+ | 0.2287 | 12750 | 0.0 | - |
583
+ | 0.2296 | 12800 | 0.0 | - |
584
+ | 0.2304 | 12850 | 0.0 | - |
585
+ | 0.2313 | 12900 | 0.0 | - |
586
+ | 0.2322 | 12950 | 0.0 | - |
587
+ | 0.2331 | 13000 | 0.0 | - |
588
+ | 0.2340 | 13050 | 0.0 | - |
589
+ | 0.2349 | 13100 | 0.0 | - |
590
+ | 0.2358 | 13150 | 0.0264 | - |
591
+ | 0.2367 | 13200 | 0.0 | - |
592
+ | 0.2376 | 13250 | 0.0 | - |
593
+ | 0.2385 | 13300 | 0.0 | - |
594
+ | 0.2394 | 13350 | 0.0 | - |
595
+ | 0.2403 | 13400 | 0.0 | - |
596
+ | 0.2412 | 13450 | 0.0 | - |
597
+ | 0.2421 | 13500 | 0.0 | - |
598
+ | 0.2430 | 13550 | 0.0 | - |
599
+ | 0.2439 | 13600 | 0.0 | - |
600
+ | 0.2448 | 13650 | 0.0 | - |
601
+ | 0.2457 | 13700 | 0.0 | - |
602
+ | 0.2466 | 13750 | 0.0 | - |
603
+ | 0.2475 | 13800 | 0.0 | - |
604
+ | 0.2484 | 13850 | 0.0 | - |
605
+ | 0.2493 | 13900 | 0.0 | - |
606
+ | 0.2502 | 13950 | 0.0 | - |
607
+ | 0.2511 | 14000 | 0.0 | - |
608
+ | 0.2520 | 14050 | 0.0 | - |
609
+ | 0.2529 | 14100 | 0.0 | - |
610
+ | 0.2538 | 14150 | 0.0001 | - |
611
+ | 0.2547 | 14200 | 0.0 | - |
612
+ | 0.2556 | 14250 | 0.0 | - |
613
+ | 0.2565 | 14300 | 0.0 | - |
614
+ | 0.2573 | 14350 | 0.0 | - |
615
+ | 0.2582 | 14400 | 0.0 | - |
616
+ | 0.2591 | 14450 | 0.0 | - |
617
+ | 0.2600 | 14500 | 0.0 | - |
618
+ | 0.2609 | 14550 | 0.0001 | - |
619
+ | 0.2618 | 14600 | 0.0 | - |
620
+ | 0.2627 | 14650 | 0.0 | - |
621
+ | 0.2636 | 14700 | 0.0 | - |
622
+ | 0.2645 | 14750 | 0.0 | - |
623
+ | 0.2654 | 14800 | 0.0 | - |
624
+ | 0.2663 | 14850 | 0.0 | - |
625
+ | 0.2672 | 14900 | 0.0 | - |
626
+ | 0.2681 | 14950 | 0.0001 | - |
627
+ | 0.2690 | 15000 | 0.0 | - |
628
+ | 0.2699 | 15050 | 0.0 | - |
629
+ | 0.2708 | 15100 | 0.0 | - |
630
+ | 0.2717 | 15150 | 0.0 | - |
631
+ | 0.2726 | 15200 | 0.0 | - |
632
+ | 0.2735 | 15250 | 0.0 | - |
633
+ | 0.2744 | 15300 | 0.0 | - |
634
+ | 0.2753 | 15350 | 0.0 | - |
635
+ | 0.2762 | 15400 | 0.0 | - |
636
+ | 0.2771 | 15450 | 0.0 | - |
637
+ | 0.2780 | 15500 | 0.0001 | - |
638
+ | 0.2789 | 15550 | 0.0621 | - |
639
+ | 0.2798 | 15600 | 0.0056 | - |
640
+ | 0.2807 | 15650 | 0.0 | - |
641
+ | 0.2816 | 15700 | 0.0 | - |
642
+ | 0.2825 | 15750 | 0.0145 | - |
643
+ | 0.2834 | 15800 | 0.0 | - |
644
+ | 0.2842 | 15850 | 0.0 | - |
645
+ | 0.2851 | 15900 | 0.0166 | - |
646
+ | 0.2860 | 15950 | 0.0 | - |
647
+ | 0.2869 | 16000 | 0.0 | - |
648
+ | 0.2878 | 16050 | 0.0 | - |
649
+ | 0.2887 | 16100 | 0.0166 | - |
650
+ | 0.2896 | 16150 | 0.0 | - |
651
+ | 0.2905 | 16200 | 0.0 | - |
652
+ | 0.2914 | 16250 | 0.0169 | - |
653
+ | 0.2923 | 16300 | 0.0 | - |
654
+ | 0.2932 | 16350 | 0.0 | - |
655
+ | 0.2941 | 16400 | 0.0 | - |
656
+ | 0.2950 | 16450 | 0.0 | - |
657
+ | 0.2959 | 16500 | 0.0 | - |
658
+ | 0.2968 | 16550 | 0.0 | - |
659
+ | 0.2977 | 16600 | 0.0 | - |
660
+ | 0.2986 | 16650 | 0.0 | - |
661
+ | 0.2995 | 16700 | 0.0 | - |
662
+ | 0.3004 | 16750 | 0.0 | - |
663
+ | 0.3013 | 16800 | 0.0 | - |
664
+ | 0.3022 | 16850 | 0.0 | - |
665
+ | 0.3031 | 16900 | 0.0 | - |
666
+ | 0.3040 | 16950 | 0.0 | - |
667
+ | 0.3049 | 17000 | 0.0 | - |
668
+ | 0.3058 | 17050 | 0.0138 | - |
669
+ | 0.3067 | 17100 | 0.0 | - |
670
+ | 0.3076 | 17150 | 0.0 | - |
671
+ | 0.3085 | 17200 | 0.0 | - |
672
+ | 0.3094 | 17250 | 0.0 | - |
673
+ | 0.3103 | 17300 | 0.0 | - |
674
+ | 0.3111 | 17350 | 0.0 | - |
675
+ | 0.3120 | 17400 | 0.0 | - |
676
+ | 0.3129 | 17450 | 0.0001 | - |
677
+ | 0.3138 | 17500 | 0.0 | - |
678
+ | 0.3147 | 17550 | 0.0 | - |
679
+ | 0.3156 | 17600 | 0.0 | - |
680
+ | 0.3165 | 17650 | 0.0 | - |
681
+ | 0.3174 | 17700 | 0.0 | - |
682
+ | 0.3183 | 17750 | 0.0 | - |
683
+ | 0.3192 | 17800 | 0.0 | - |
684
+ | 0.3201 | 17850 | 0.0 | - |
685
+ | 0.3210 | 17900 | 0.0 | - |
686
+ | 0.3219 | 17950 | 0.0001 | - |
687
+ | 0.3228 | 18000 | 0.0 | - |
688
+ | 0.3237 | 18050 | 0.0 | - |
689
+ | 0.3246 | 18100 | 0.0 | - |
690
+ | 0.3255 | 18150 | 0.0 | - |
691
+ | 0.3264 | 18200 | 0.0 | - |
692
+ | 0.3273 | 18250 | 0.0 | - |
693
+ | 0.3282 | 18300 | 0.0 | - |
694
+ | 0.3291 | 18350 | 0.0 | - |
695
+ | 0.3300 | 18400 | 0.0 | - |
696
+ | 0.3309 | 18450 | 0.0003 | - |
697
+ | 0.3318 | 18500 | 0.0 | - |
698
+ | 0.3327 | 18550 | 0.0 | - |
699
+ | 0.3336 | 18600 | 0.0 | - |
700
+ | 0.3345 | 18650 | 0.0 | - |
701
+ | 0.3354 | 18700 | 0.0 | - |
702
+ | 0.3363 | 18750 | 0.0 | - |
703
+ | 0.3372 | 18800 | 0.0 | - |
704
+ | 0.3380 | 18850 | 0.0 | - |
705
+ | 0.3389 | 18900 | 0.0 | - |
706
+ | 0.3398 | 18950 | 0.0 | - |
707
+ | 0.3407 | 19000 | 0.0 | - |
708
+ | 0.3416 | 19050 | 0.0 | - |
709
+ | 0.3425 | 19100 | 0.0 | - |
710
+ | 0.3434 | 19150 | 0.0 | - |
711
+ | 0.3443 | 19200 | 0.0 | - |
712
+ | 0.3452 | 19250 | 0.0 | - |
713
+ | 0.3461 | 19300 | 0.0 | - |
714
+ | 0.3470 | 19350 | 0.0 | - |
715
+ | 0.3479 | 19400 | 0.0 | - |
716
+ | 0.3488 | 19450 | 0.0 | - |
717
+ | 0.3497 | 19500 | 0.0001 | - |
718
+ | 0.3506 | 19550 | 0.0131 | - |
719
+ | 0.3515 | 19600 | 0.0 | - |
720
+ | 0.3524 | 19650 | 0.0 | - |
721
+ | 0.3533 | 19700 | 0.0 | - |
722
+ | 0.3542 | 19750 | 0.0 | - |
723
+ | 0.3551 | 19800 | 0.0 | - |
724
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725
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726
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727
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728
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729
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730
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731
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732
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733
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734
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735
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736
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737
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738
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739
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740
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741
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742
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743
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744
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745
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746
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747
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748
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749
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750
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751
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753
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754
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755
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756
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757
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758
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759
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760
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761
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762
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763
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764
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765
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766
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767
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768
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769
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770
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771
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772
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773
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774
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775
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776
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777
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778
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779
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780
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781
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782
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783
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784
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785
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786
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788
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789
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790
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791
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792
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793
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794
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796
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797
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798
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799
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800
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801
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802
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803
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804
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805
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809
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810
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811
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812
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813
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814
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815
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816
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817
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818
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819
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820
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823
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824
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825
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828
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829
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830
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831
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832
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833
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834
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836
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837
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838
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839
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840
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842
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843
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846
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847
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848
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850
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851
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852
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853
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854
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855
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856
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857
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860
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861
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862
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863
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864
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865
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866
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867
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868
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869
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870
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871
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872
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873
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874
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875
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876
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877
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878
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879
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880
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881
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882
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883
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884
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885
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886
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887
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888
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889
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890
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891
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892
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893
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894
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896
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897
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898
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900
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901
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902
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903
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904
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905
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906
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907
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908
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909
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910
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911
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912
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913
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914
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915
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916
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917
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918
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919
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920
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921
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923
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924
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925
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928
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929
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930
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931
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932
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933
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934
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935
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936
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937
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938
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939
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940
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941
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942
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943
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944
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945
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948
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949
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950
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951
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952
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953
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954
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955
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956
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957
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958
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959
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960
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962
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963
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964
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966
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967
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968
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969
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970
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971
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972
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973
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976
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977
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978
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979
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980
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981
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983
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984
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985
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986
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987
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988
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989
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990
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991
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992
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993
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994
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995
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996
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997
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998
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999
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1000
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1001
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1002
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1003
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1004
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1005
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1006
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1007
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1008
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1009
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1010
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1011
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1012
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1013
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1014
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1015
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1016
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1017
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1018
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1019
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1020
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1021
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1022
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1023
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1024
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1025
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1026
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1027
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1028
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1029
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1030
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1031
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1032
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1033
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1034
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1035
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1036
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1037
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1038
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1039
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1040
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1041
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1042
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1043
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1044
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1045
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1046
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1047
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1048
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1049
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1050
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1051
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1052
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1053
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1054
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1055
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1056
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1057
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1058
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1059
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1060
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1061
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1062
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1063
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1064
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1065
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1066
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1067
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1068
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1069
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1070
+ | 0.6662 | 37150 | 0.0 | - |
1071
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1072
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1073
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1074
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1075
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1076
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1077
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1078
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1079
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1080
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1081
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1082
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1083
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1084
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1085
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1086
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1087
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1088
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1089
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1090
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1091
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1092
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1093
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1094
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1095
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1096
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1097
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1098
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1099
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1100
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1101
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1102
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1366
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1367
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1371
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1372
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1377
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1379
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1380
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1381
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1382
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1383
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1384
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1385
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1386
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1387
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1388
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1389
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1390
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1391
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1392
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1393
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1395
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1396
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1397
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1398
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1399
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1400
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1401
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1402
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1403
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1404
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1405
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1406
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1407
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1408
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1409
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1410
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1411
+ | 0.9720 | 54200 | 0.0 | - |
1412
+ | 0.9729 | 54250 | 0.0 | - |
1413
+ | 0.9738 | 54300 | 0.0 | - |
1414
+ | 0.9747 | 54350 | 0.0 | - |
1415
+ | 0.9756 | 54400 | 0.0 | - |
1416
+ | 0.9765 | 54450 | 0.0 | - |
1417
+ | 0.9774 | 54500 | 0.0 | - |
1418
+ | 0.9783 | 54550 | 0.0 | - |
1419
+ | 0.9792 | 54600 | 0.0 | - |
1420
+ | 0.9801 | 54650 | 0.0 | - |
1421
+ | 0.9810 | 54700 | 0.0 | - |
1422
+ | 0.9819 | 54750 | 0.0 | - |
1423
+ | 0.9828 | 54800 | 0.0 | - |
1424
+ | 0.9837 | 54850 | 0.0 | - |
1425
+ | 0.9846 | 54900 | 0.0 | - |
1426
+ | 0.9855 | 54950 | 0.0 | - |
1427
+ | 0.9864 | 55000 | 0.0 | - |
1428
+ | 0.9872 | 55050 | 0.0 | - |
1429
+ | 0.9881 | 55100 | 0.0156 | - |
1430
+ | 0.9890 | 55150 | 0.0 | - |
1431
+ | 0.9899 | 55200 | 0.0 | - |
1432
+ | 0.9908 | 55250 | 0.0 | - |
1433
+ | 0.9917 | 55300 | 0.0 | - |
1434
+ | 0.9926 | 55350 | 0.0 | - |
1435
+ | 0.9935 | 55400 | 0.0 | - |
1436
+ | 0.9944 | 55450 | 0.0 | - |
1437
+ | 0.9953 | 55500 | 0.0 | - |
1438
+ | 0.9962 | 55550 | 0.0 | - |
1439
+ | 0.9971 | 55600 | 0.0 | - |
1440
+ | 0.9980 | 55650 | 0.0 | - |
1441
+ | 0.9989 | 55700 | 0.0 | - |
1442
+ | 0.9998 | 55750 | 0.0 | - |
1443
+
1444
+ ### Framework Versions
1445
+ - Python: 3.10.12
1446
+ - SetFit: 1.0.3
1447
+ - Sentence Transformers: 2.7.0
1448
+ - spaCy: 3.7.4
1449
+ - Transformers: 4.40.1
1450
+ - PyTorch: 2.2.1+cu121
1451
+ - Datasets: 2.19.0
1452
+ - Tokenizers: 0.19.1
1453
+
1454
+ ## Citation
1455
+
1456
+ ### BibTeX
1457
+ ```bibtex
1458
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
1459
+ doi = {10.48550/ARXIV.2209.11055},
1460
+ url = {https://arxiv.org/abs/2209.11055},
1461
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
1462
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
1463
+ title = {Efficient Few-Shot Learning Without Prompts},
1464
+ publisher = {arXiv},
1465
+ year = {2022},
1466
+ copyright = {Creative Commons Attribution 4.0 International}
1467
+ }
1468
+ ```
1469
+
1470
+ <!--
1471
+ ## Glossary
1472
+
1473
+ *Clearly define terms in order to be accessible across audiences.*
1474
+ -->
1475
+
1476
+ <!--
1477
+ ## Model Card Authors
1478
+
1479
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1480
+ -->
1481
+
1482
+ <!--
1483
+ ## Model Card Contact
1484
+
1485
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1486
+ -->
config.json ADDED
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