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waterabbit114
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my-setfit-classifier_obscene

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Model card Files Community
waterabbit114 commited on
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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 +341 -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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+ - accuracy
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+ widget:
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+ - text: is completely right on this. carnildo’s comment is just a waste of space.
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+ 176.12.107.140
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+ - text: '" please do not vandalize pages, as you did with this edit to bella swan.
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+ if you continue to do so, you will be blocked from editing. (talk) "'
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+ - text: ipv6 mirc doesn't natively supports ipv6 protocols. it could be enabled
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+ by adding a external dll plugin who will enable a special protocol for dns and
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+ connecting to ipv6 servers.
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+ - text: '" link thanks for fixing that disambiguation link on usher''s album )
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+ flash; "'
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+ - text: '|b-class-1= yes |b-class-2= yes |b-class-3= yes |b-class-4= yes |b-class-5=
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+ yes'
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+ pipeline_tag: text-classification
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+ inference: true
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+ base_model: sentence-transformers/paraphrase-mpnet-base-v2
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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 [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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 [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 512 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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+ | 0 | <ul><li>', metal unless it was 70s oldskool'</li><li>"do whichever you think would be best then: if you rename the account, just let me know, and i'll go over there to recreate it; but if you'd prefer to rename the account, recreate it, and send me the password which i can then change, that's fine with me."</li><li>'" no, it was a far-too-much-of-an-in-joke on the fact that principle→principal is usually one of the first things the fa regulars jump on (along with the dreaded spaced em dash)\xa0–\xa0scent "'</li></ul> |
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+ | 1 | <ul><li>"oh, no! i just read the vile diatribe you left for me on my user page. no, you get no respect you ass little shit fuck you, you're an unhappy little dick puller!"</li><li>'fuck you youfuckingidiot'</li><li>"hey , you are a chicken shit coward i told you that everytime you had one of your administrator buddies block me, i would quickly be back on with a new ip address editing your vandalism of this article. i meant it!!! why don't you stop masturbating to wikipedia and get a real life? i told you that you don't know who you're fuck with!!!"</li></ul> |
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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("waterabbit114/my-setfit-classifier_obscene")
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+ # Run inference
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+ preds = model("\" link thanks for fixing that disambiguation link on usher's album ) flash; \"")
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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 | 3 | 57.2 | 426 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 10 |
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+ | 1 | 10 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (1, 1)
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+ - num_epochs: (10, 10)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 20
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+ - body_learning_rate: (2e-05, 2e-05)
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+ - head_learning_rate: 2e-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: 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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+ | 0.0013 | 1 | 0.1758 | - |
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+ | 0.0625 | 50 | 0.0036 | - |
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+ | 0.125 | 100 | 0.1383 | - |
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+ | 0.1875 | 150 | 0.0148 | - |
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+ | 0.25 | 200 | 0.0216 | - |
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+ | 0.3125 | 250 | 0.0001 | - |
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+ | 0.375 | 300 | 0.0021 | - |
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+ | 0.4375 | 350 | 0.001 | - |
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+ | 0.5 | 400 | 0.0015 | - |
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+ | 0.5625 | 450 | 0.0004 | - |
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+ | 0.625 | 500 | 0.0 | - |
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+ | 0.6875 | 550 | 0.0003 | - |
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+ | 0.75 | 600 | 0.0 | - |
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+ | 0.8125 | 650 | 0.0 | - |
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+ | 0.875 | 700 | 0.0 | - |
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+ | 0.9375 | 750 | 0.0001 | - |
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+ | 1.0 | 800 | 0.0 | - |
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+ | 1.0625 | 850 | 0.0 | - |
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+ | 1.125 | 900 | 0.0002 | - |
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+ | 1.1875 | 950 | 0.0 | - |
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+ | 1.25 | 1000 | 0.0008 | - |
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+ | 1.3125 | 1050 | 0.0002 | - |
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+ | 1.375 | 1100 | 0.0 | - |
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+ | 1.4375 | 1150 | 0.0 | - |
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+ | 1.5 | 1200 | 0.0 | - |
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+ | 1.5625 | 1250 | 0.0001 | - |
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+ | 1.625 | 1300 | 0.0 | - |
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+ | 1.6875 | 1350 | 0.0 | - |
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+ | 1.75 | 1400 | 0.0 | - |
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+ | 1.8125 | 1450 | 0.0 | - |
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+ | 1.875 | 1500 | 0.0 | - |
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+ | 1.9375 | 1550 | 0.0 | - |
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+ | 2.0 | 1600 | 0.0 | - |
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+ | 2.0625 | 1650 | 0.0001 | - |
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+ | 2.125 | 1700 | 0.0001 | - |
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+ | 2.1875 | 1750 | 0.0 | - |
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+ | 2.25 | 1800 | 0.0001 | - |
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+ | 2.3125 | 1850 | 0.0001 | - |
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+ | 2.375 | 1900 | 0.0002 | - |
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+ | 2.4375 | 1950 | 0.0 | - |
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+ | 2.5 | 2000 | 0.0001 | - |
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+ | 2.5625 | 2050 | 0.0001 | - |
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+ | 2.625 | 2100 | 0.0 | - |
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+ | 2.6875 | 2150 | 0.0001 | - |
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+ | 2.75 | 2200 | 0.0003 | - |
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+ | 2.9375 | 2350 | 0.0 | - |
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+ | 3.0 | 2400 | 0.0003 | - |
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+ | 3.4375 | 2750 | 0.0 | - |
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+ | 3.5625 | 2850 | 0.0 | - |
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+ | 3.625 | 2900 | 0.0001 | - |
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+ | 3.6875 | 2950 | 0.0 | - |
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+ | 3.75 | 3000 | 0.0001 | - |
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+ | 3.8125 | 3050 | 0.0 | - |
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+ | 3.9375 | 3150 | 0.0 | - |
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+ | 4.0 | 3200 | 0.0 | - |
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+ | 4.0625 | 3250 | 0.0 | - |
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+ | 4.125 | 3300 | 0.0 | - |
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+ | 4.1875 | 3350 | 0.0 | - |
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+ | 4.25 | 3400 | 0.0 | - |
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+ | 4.3125 | 3450 | 0.0 | - |
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+ | 4.375 | 3500 | 0.0001 | - |
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+ | 4.4375 | 3550 | 0.0001 | - |
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+ | 4.5 | 3600 | 0.0 | - |
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+ | 4.5625 | 3650 | 0.0 | - |
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+ | 4.625 | 3700 | 0.0 | - |
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+ | 4.6875 | 3750 | 0.0 | - |
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+ | 4.75 | 3800 | 0.0001 | - |
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+ | 4.8125 | 3850 | 0.0 | - |
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+ | 4.875 | 3900 | 0.0 | - |
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+ | 4.9375 | 3950 | 0.0 | - |
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+ | 5.0 | 4000 | 0.0 | - |
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+ | 8.75 | 7000 | 0.0 | - |
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+ | 9.4375 | 7550 | 0.0 | - |
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+ | 9.5 | 7600 | 0.0 | - |
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+ | 9.5625 | 7650 | 0.0 | - |
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+ | 9.625 | 7700 | 0.0 | - |
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+ | 10.0 | 8000 | 0.0 | - |
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+
300
+ ### Framework Versions
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+ - Python: 3.11.7
302
+ - SetFit: 1.0.3
303
+ - Sentence Transformers: 2.2.2
304
+ - Transformers: 4.35.2
305
+ - PyTorch: 2.1.1+cu121
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+ - Datasets: 2.14.5
307
+ - Tokenizers: 0.15.1
308
+
309
+ ## Citation
310
+
311
+ ### BibTeX
312
+ ```bibtex
313
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
314
+ doi = {10.48550/ARXIV.2209.11055},
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+ 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}
322
+ }
323
+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
329
+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
335
+ -->
336
+
337
+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
config.json ADDED
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+ {
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+ "_name_or_path": "/root/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-mpnet-base-v2/",
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+ "architectures": [
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+ "MPNetModel"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "bos_token_id": 0,
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+ "eos_token_id": 2,
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+ "hidden_act": "gelu",
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+ "hidden_size": 768,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "layer_norm_eps": 1e-05,
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+ "max_position_embeddings": 514,
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+ "model_type": "mpnet",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 1,
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+ "relative_attention_num_buckets": 32,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.35.2",
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+ "vocab_size": 30527
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+ }
config_sentence_transformers.json ADDED
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+ "__version__": {
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+ "sentence_transformers": "2.0.0",
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+ "transformers": "4.7.0",
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+ "pytorch": "1.9.0+cu102"
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+ }
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+ }
config_setfit.json ADDED
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+ "labels": null,
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+ "normalize_embeddings": false
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+ }
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+ "type": "sentence_transformers.models.Pooling"
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sentence_bert_config.json ADDED
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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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