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anismahmahi
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G2_replace_Whata_repetition_with_noPropaganda_SetFit

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
Model card Files Community
anismahmahi 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 +360 -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 +66 -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: Fox News, The Washington Post, NBC News, The Associated Press and the Los
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+ Angeles Times are among the entities that have said they will file amicus briefs
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+ on behalf of CNN.
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+ - text: 'Tommy Robinson is in prison today because he violated a court order demanding
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+ that he not film videos outside the trials of Muslim rape gangs.
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+
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+ '
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+ - text: As I wrote during the presidential campaign, Trump has no idea of Washington
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+ and no idea who to appoint who would support him rather than work against him.
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+ - text: IN MAY 2013, the Washington Post’s Greg Miller reported that the head of the
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+ CIA’s clandestine service was being shifted out of that position as a result of
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+ “a management shake-up” by then-Director John Brennan.
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+ - text: Columbus police are investigating the shootings.
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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: accuracy
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+ value: 0.602089552238806
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+ name: Accuracy
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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 OneVsRestClassifier 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 OneVsRestClassifier 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 | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.6021 |
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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("anismahmahi/G2_replace_Whata_repetition_with_noPropaganda_SetFit")
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+ # Run inference
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+ preds = model("Columbus police are investigating the shootings.")
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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 | 23.1093 | 129 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 16)
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+ - num_epochs: (2, 2)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 10
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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: True
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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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+ | 0.0002 | 1 | 0.3592 | - |
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+ | 0.0121 | 50 | 0.2852 | - |
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+ | 0.0243 | 100 | 0.2694 | - |
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+ | 0.0364 | 150 | 0.2182 | - |
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+ | 0.0486 | 200 | 0.2224 | - |
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+ | 0.0607 | 250 | 0.2634 | - |
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+ | 0.0729 | 300 | 0.2431 | - |
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+ | 0.0850 | 350 | 0.2286 | - |
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+ | 0.0971 | 400 | 0.197 | - |
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+ | 0.1093 | 450 | 0.2466 | - |
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+ | 0.1214 | 500 | 0.2374 | - |
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+ | 0.1336 | 550 | 0.2134 | - |
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+ | 0.1457 | 600 | 0.2092 | - |
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+ | 0.1578 | 650 | 0.1987 | - |
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+ | 0.1700 | 700 | 0.2288 | - |
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+ | 0.1821 | 750 | 0.1562 | - |
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+ | 0.1943 | 800 | 0.27 | - |
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+ | 0.2064 | 850 | 0.1314 | - |
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+ | 0.2186 | 900 | 0.2144 | - |
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+ | 0.2307 | 950 | 0.184 | - |
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+ | 0.2428 | 1000 | 0.2069 | - |
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+ | 0.2550 | 1050 | 0.1773 | - |
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+ | 0.2671 | 1100 | 0.0704 | - |
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+ | 0.2793 | 1150 | 0.1139 | - |
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+ | 0.2914 | 1200 | 0.2398 | - |
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+ | 0.3035 | 1250 | 0.0672 | - |
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+ | 0.3157 | 1300 | 0.1321 | - |
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+ | 0.3278 | 1350 | 0.0803 | - |
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+ | 0.3400 | 1400 | 0.0589 | - |
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+ | 0.3521 | 1450 | 0.0428 | - |
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+ | **1.0** | **4118** | **-** | **0.2746** |
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+ | 1.9913 | 8200 | 0.0002 | - |
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+ | 2.0 | 8236 | - | 0.2768 |
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+
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+ * The bold row denotes the saved checkpoint.
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+ ### Framework Versions
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+ - Python: 3.10.12
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+ - SetFit: 1.0.1
322
+ - Sentence Transformers: 2.2.2
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+ - Transformers: 4.35.2
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+ - PyTorch: 2.1.0+cu121
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+ - Datasets: 2.16.1
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+ - Tokenizers: 0.15.0
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+
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+ ## Citation
329
+
330
+ ### BibTeX
331
+ ```bibtex
332
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
333
+ 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},
337
+ title = {Efficient Few-Shot Learning Without Prompts},
338
+ publisher = {arXiv},
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+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
341
+ }
342
+ ```
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+
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+ <!--
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+ ## Glossary
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+
347
+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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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.*
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+ -->
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+
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+ <!--
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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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+ "max_position_embeddings": 514,
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+ "model_type": "mpnet",
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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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+ }
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2
+ "max_seq_length": 512,
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+ "do_lower_case": false
4
+ }
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": false,
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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": {
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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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+ },
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+ "pad_token": {
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+ "content": "<pad>",
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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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+ "sep_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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+ "unk_token": {
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+ "content": "[UNK]",
46
+ "lstrip": false,
47
+ "normalized": false,
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+ "rstrip": false,
49
+ "single_word": false
50
+ }
51
+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
@@ -0,0 +1,66 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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+ "rstrip": 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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+ "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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+ "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,
49
+ "eos_token": "</s>",
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+ "mask_token": "<mask>",
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+ "max_length": 512,
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+ "model_max_length": 512,
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+ "never_split": null,
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+ "pad_to_multiple_of": null,
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+ "pad_token": "<pad>",
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+ "pad_token_type_id": 0,
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+ "padding_side": "right",
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+ "sep_token": "</s>",
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+ "stride": 0,
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+ "strip_accents": null,
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+ "tokenize_chinese_chars": true,
62
+ "tokenizer_class": "MPNetTokenizer",
63
+ "truncation_side": "right",
64
+ "truncation_strategy": "longest_first",
65
+ "unk_token": "[UNK]"
66
+ }
vocab.txt ADDED
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