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CrisisNarratives
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setfit-13classes-multi_label

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

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Files changed (13) hide show
  1. 1_Pooling/config.json +7 -0
  2. README.md +354 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +7 -0
  5. config_setfit.json +4 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +51 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +59 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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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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+ }
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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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ metrics:
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+ - metric
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+ widget:
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+ - text: A combined 20 million people per year die of smoking and hunger, so authorities
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+ can't seem to feed people and they allow you to buy cigarettes but we are facing
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+ another lockdown for a virus that has a 99.5% survival rate!!! THINK PEOPLE. LOOK
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+ AT IT LOGICALLY WITH YOUR OWN EYES.
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+ - text: Scientists do not agree on the consequences of climate change, nor is there
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+ any consensus on that subject. The predictions on that from are just ascientific
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+ speculation. Bring on the warming."
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+ - text: If Tam is our "top doctor"....I am going back to leaches and voodoo...just
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+ as much science in that as the crap she spouts
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+ - text: "Can she skip school by herself and sit infront of parliament? \r\n Fake emotions\
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+ \ and just a good actor."
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+ - text: my dad had huge ones..so they may be real..
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+ pipeline_tag: text-classification
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+ inference: false
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+ base_model: sentence-transformers/paraphrase-mpnet-base-v2
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+ model-index:
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+ - name: SetFit with sentence-transformers/paraphrase-mpnet-base-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: metric
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+ value: 0.65694899973345
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+ name: Metric
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+ ---
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+
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+ # SetFit with sentence-transformers/paraphrase-mpnet-base-v2
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A ClassifierChain instance is used for classification.
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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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+ ## 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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
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+ - **Classification head:** a ClassifierChain instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ <!-- - **Number of Classes:** Unknown -->
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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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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Metric |
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+ |:--------|:-------|
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+ | **all** | 0.6569 |
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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 SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("CrisisNarratives/setfit-13classes-multi_label")
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+ # Run inference
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+ preds = model("my dad had huge ones..so they may be real..")
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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 | 1 | 25.8891 | 1681 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 16)
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+ - num_epochs: (3, 3)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 40
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+ - body_learning_rate: (1.752e-05, 1.752e-05)
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+ - head_learning_rate: 1.752e-05
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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: 30
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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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+ | 0.0004 | 1 | 0.3059 | - |
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+ | 0.0185 | 50 | 0.3597 | - |
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+ | 0.0370 | 100 | 0.272 | - |
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+ | 0.0555 | 150 | 0.2282 | - |
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+ | 0.0739 | 200 | 0.2413 | - |
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+ | 0.0924 | 250 | 0.2239 | - |
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+ | 0.1109 | 300 | 0.2447 | - |
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+ | 0.1294 | 350 | 0.1574 | - |
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+ | 0.1479 | 400 | 0.1873 | - |
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+ | 0.1664 | 450 | 0.1537 | - |
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+ | 0.1848 | 500 | 0.1661 | - |
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+ | 0.2033 | 550 | 0.1692 | - |
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+ | 0.2218 | 600 | 0.1105 | - |
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+ | 0.2403 | 650 | 0.1316 | - |
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+ | 0.2588 | 700 | 0.1018 | - |
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+ | 0.2773 | 750 | 0.1148 | - |
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+ | 0.2957 | 800 | 0.0588 | - |
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+ | 0.3142 | 850 | 0.2385 | - |
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+ | 0.3327 | 900 | 0.0302 | - |
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+ | 0.3512 | 950 | 0.0714 | - |
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+ | 0.3697 | 1000 | 0.1587 | - |
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+ | 0.3882 | 1050 | 0.1479 | - |
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+ | 0.4067 | 1100 | 0.0897 | - |
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+ | 0.4251 | 1150 | 0.064 | - |
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+ | 0.4436 | 1200 | 0.0774 | - |
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+ | 0.4621 | 1250 | 0.0318 | - |
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+ | 0.4806 | 1300 | 0.1231 | - |
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+ | 0.4991 | 1350 | 0.0983 | - |
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+ | 0.5176 | 1400 | 0.1537 | - |
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+ | 0.5360 | 1450 | 0.1382 | - |
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+ | 0.8133 | 2200 | 0.0793 | - |
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+ | 0.9242 | 2500 | 0.1726 | - |
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+ | 0.9427 | 2550 | 0.0491 | - |
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+ | 1.4787 | 4000 | 0.0772 | - |
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+ | 1.4972 | 4050 | 0.0466 | - |
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+ | 1.5342 | 4150 | 0.0489 | - |
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+ | 1.5527 | 4200 | 0.0273 | - |
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+ | 1.5712 | 4250 | 0.0335 | - |
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+ | 1.5896 | 4300 | 0.0733 | - |
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+ | 2.4214 | 6550 | 0.033 | - |
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+ | 2.4584 | 6650 | 0.0434 | - |
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+ | 2.7726 | 7500 | 0.0549 | - |
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+ | 2.7911 | 7550 | 0.041 | - |
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+ | 2.8281 | 7650 | 0.0249 | - |
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+ | 2.8466 | 7700 | 0.0807 | - |
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+ | 2.8651 | 7750 | 0.0268 | - |
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+ | 2.8835 | 7800 | 0.0306 | - |
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+ | 2.9390 | 7950 | 0.0454 | - |
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+ | 2.9945 | 8100 | 0.0452 | - |
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+
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+ ### Framework Versions
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+ - Python: 3.9.16
315
+ - SetFit: 1.0.1
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+ - Sentence Transformers: 2.2.2
317
+ - Transformers: 4.35.0
318
+ - PyTorch: 2.1.0+cu121
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+ - Datasets: 2.14.6
320
+ - Tokenizers: 0.14.1
321
+
322
+ ## Citation
323
+
324
+ ### BibTeX
325
+ ```bibtex
326
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
327
+ doi = {10.48550/ARXIV.2209.11055},
328
+ url = {https://arxiv.org/abs/2209.11055},
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+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
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+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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+ title = {Efficient Few-Shot Learning Without Prompts},
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+ publisher = {arXiv},
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+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
335
+ }
336
+ ```
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+
338
+ <!--
339
+ ## Glossary
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+
341
+ *Clearly define terms in order to be accessible across audiences.*
342
+ -->
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+
344
+ <!--
345
+ ## Model Card Authors
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+
347
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
348
+ -->
349
+
350
+ <!--
351
+ ## Model Card Contact
352
+
353
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
354
+ -->
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+ "MPNetModel"
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.35.0",
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+ "vocab_size": 30527
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+ }
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special_tokens_map.json ADDED
@@ -0,0 +1,51 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "bos_token": {
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+ "content": "<s>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "cls_token": {
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+ "content": "<s>",
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+ "lstrip": false,
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+ "normalized": true,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "eos_token": {
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+ "content": "</s>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "mask_token": {
24
+ "content": "<mask>",
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+ "lstrip": true,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "pad_token": {
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+ "content": "<pad>",
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+ "lstrip": false,
33
+ "normalized": false,
34
+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "sep_token": {
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+ "content": "</s>",
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+ "lstrip": false,
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+ "normalized": true,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "unk_token": {
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+ "content": "[UNK]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ }
51
+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
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+ "content": "<s>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "1": {
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+ "content": "<pad>",
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "2": {
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+ "content": "</s>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "104": {
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+ "content": "[UNK]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "30526": {
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+ "content": "<mask>",
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+ "lstrip": true,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ }
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+ },
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+ "bos_token": "<s>",
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+ "clean_up_tokenization_spaces": true,
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+ "cls_token": "<s>",
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+ "do_basic_tokenize": true,
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+ "do_lower_case": true,
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+ "eos_token": "</s>",
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+ "mask_token": "<mask>",
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+ "model_max_length": 512,
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+ "never_split": null,
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+ "pad_token": "<pad>",
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+ "sep_token": "</s>",
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+ "strip_accents": null,
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+ "tokenize_chinese_chars": true,
57
+ "tokenizer_class": "MPNetTokenizer",
58
+ "unk_token": "[UNK]"
59
+ }
vocab.txt ADDED
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