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HussienAhmad
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SFT_GradProject

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Model card Files
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HussienAhmad commited on
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Browse files
Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +968 -3
  3. config.json +26 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +11 -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 CHANGED
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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: sentence-transformers/all-MiniLM-L6-v2
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+ library_name: setfit
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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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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+ widget:
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+ - text: Good morning
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+ - text: how does the recommendation system work on this platform
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+ - text: who are you
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+ - text: where is the search bar
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+ - text: how can I find courses related to programming
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+ inference: true
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+ model-index:
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+ - name: SetFit 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: 0.8333333333333334
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+ name: Accuracy
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+ ---
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+
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+ # SetFit 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 Text Classification. 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.
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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/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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+ - **Maximum Sequence Length:** 256 tokens
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+ - **Number of Classes:** 6 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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+ | general-questions | <ul><li>'can you explain the concept of cloud computing'</li><li>'how do I assess my skills after completing a course'</li><li>'what is the significance of feedback in online learning'</li></ul> |
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+ | website-information | <ul><li>'how to access the dashboard'</li><li>'where can I see my completed courses'</li><li>'where can I find notifications'</li></ul> |
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+ | greet-who_are_you | <ul><li>"pourquoi j'ai besoin de toi"</li><li>'help please'</li><li>'I can not understand you'</li></ul> |
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+ | recommendations | <ul><li>'how do I get recommendations based on my interests'</li><li>'can you recommend advanced courses in data science'</li><li>'what courses are trending in web development'</li></ul> |
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+ | greet-hi | <ul><li>'Hey'</li><li>'Bonsoir'</li><li>'Salut'</li></ul> |
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+ | greet-good_bye | <ul><li>'sortir'</li><li>'A plus tard'</li><li>'See you later'</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** | 0.8333 |
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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("HussienAhmad/SFT_GradProject")
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+ # Run inference
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+ preds = model("who are you")
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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 | 6.2 | 11 |
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+
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+ | Label | Training Sample Count |
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+ |:--------------------|:----------------------|
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+ | greet-hi | 5 |
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+ | greet-who_are_you | 7 |
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+ | greet-good_bye | 5 |
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+ | general-questions | 28 |
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+ | recommendations | 27 |
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+ | website-information | 28 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (4, 4)
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+ - num_epochs: (4, 4)
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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: 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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+ | 0.0005 | 1 | 0.3442 | - |
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+ | 0.0053 | 10 | 0.2974 | - |
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+ | 0.0105 | 20 | 0.1983 | - |
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+ | 0.0158 | 30 | 0.0645 | - |
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+ | 0.0210 | 40 | 0.3592 | - |
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+ | 0.0263 | 50 | 0.0033 | - |
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+ | 0.0316 | 60 | 0.2558 | - |
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+ | 0.0368 | 70 | 0.2319 | - |
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+ | 0.0421 | 80 | 0.3831 | - |
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+ | 0.0473 | 90 | 0.1864 | - |
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+ | 0.0526 | 100 | 0.2244 | - |
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+ | 0.0579 | 110 | 0.2316 | - |
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+ | 0.0631 | 120 | 0.3702 | - |
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+ | 0.0684 | 130 | 0.0582 | - |
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+ | 0.0736 | 140 | 0.1031 | - |
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+ | 0.0789 | 150 | 0.2882 | - |
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+ | 0.0842 | 160 | 0.1125 | - |
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+ | 0.0894 | 170 | 0.1588 | - |
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+ | 0.0947 | 180 | 0.1672 | - |
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+ | 0.0999 | 190 | 0.0974 | - |
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+ | 0.1052 | 200 | 0.1789 | - |
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+ | 0.1105 | 210 | 0.1032 | - |
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+ | 0.1157 | 220 | 0.1344 | - |
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+ | 0.1210 | 230 | 0.0952 | - |
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+ | 0.1262 | 240 | 0.0891 | - |
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+ | 0.1315 | 250 | 0.4312 | - |
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+ | 0.1368 | 260 | 0.0871 | - |
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+ | 0.1420 | 270 | 0.1482 | - |
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+ | 0.1473 | 280 | 0.0645 | - |
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+ | 0.1526 | 290 | 0.1214 | - |
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+ | 0.1578 | 300 | 0.186 | - |
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+ | 0.1631 | 310 | 0.0516 | - |
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+ | 0.1683 | 320 | 0.0761 | - |
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+ | 0.1736 | 330 | 0.0263 | - |
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+ | 0.1789 | 340 | 0.0588 | - |
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+ | 0.1841 | 350 | 0.016 | - |
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+ | 0.1894 | 360 | 0.0264 | - |
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+ | 0.1946 | 370 | 0.0153 | - |
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+ | 0.1999 | 380 | 0.0091 | - |
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+ | 0.2052 | 390 | 0.0347 | - |
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+ | 0.2104 | 400 | 0.0095 | - |
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+ | 0.2157 | 410 | 0.0262 | - |
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+ | 0.2209 | 420 | 0.0182 | - |
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+ | 0.2262 | 430 | 0.1407 | - |
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+ | 0.2315 | 440 | 0.1451 | - |
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+ | 0.2367 | 450 | 0.0045 | - |
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+ | 0.2420 | 460 | 0.0053 | - |
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+ | 0.2472 | 470 | 0.0038 | - |
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+ | 0.2525 | 480 | 0.1549 | - |
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+ | 0.2578 | 490 | 0.0036 | - |
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+ | 0.2630 | 500 | 0.0079 | - |
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+ | 0.2683 | 510 | 0.0065 | - |
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+ | 0.2735 | 520 | 0.005 | - |
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+ | 0.2788 | 530 | 0.0038 | - |
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+ | 0.2841 | 540 | 0.0283 | - |
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+ | 0.2893 | 550 | 0.0114 | - |
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+ | 0.2946 | 560 | 0.0012 | - |
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+ | 0.2998 | 570 | 0.0165 | - |
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+ | 0.3051 | 580 | 0.0009 | - |
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+ | 0.3104 | 590 | 0.038 | - |
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+ | 0.3156 | 600 | 0.0127 | - |
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+ | 0.3209 | 610 | 0.0019 | - |
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+ | 0.3261 | 620 | 0.003 | - |
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+ | 0.3314 | 630 | 0.0013 | - |
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+ | 0.3367 | 640 | 0.0024 | - |
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+ | 0.3419 | 650 | 0.002 | - |
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+ | 0.3472 | 660 | 0.0017 | - |
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+ | 0.3524 | 670 | 0.0074 | - |
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+ | 0.3577 | 680 | 0.0008 | - |
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+ | 0.3630 | 690 | 0.0015 | - |
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+ | 0.3682 | 700 | 0.0018 | - |
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+ | 0.3735 | 710 | 0.0009 | - |
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+ | 0.3787 | 720 | 0.0019 | - |
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+ | 0.3840 | 730 | 0.0032 | - |
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+ | 0.3893 | 740 | 0.001 | - |
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+ | 0.3945 | 750 | 0.0257 | - |
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+ | 0.3998 | 760 | 0.0018 | - |
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+ | 0.4050 | 770 | 0.001 | - |
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+ | 0.4103 | 780 | 0.0006 | - |
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+ | 0.4156 | 790 | 0.0014 | - |
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+ | 0.4208 | 800 | 0.0012 | - |
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+ | 0.4261 | 810 | 0.018 | - |
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+ | 0.4314 | 820 | 0.0013 | - |
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+ | 0.4366 | 830 | 0.0019 | - |
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+ | 0.4419 | 840 | 0.0006 | - |
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+ | 0.4471 | 850 | 0.0012 | - |
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+ | 0.4524 | 860 | 0.0011 | - |
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+ | 0.4577 | 870 | 0.001 | - |
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+ | 0.4629 | 880 | 0.0017 | - |
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+ | 0.4682 | 890 | 0.002 | - |
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+ | 0.4734 | 900 | 0.0009 | - |
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+ | 0.4787 | 910 | 0.0026 | - |
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+ | 0.4840 | 920 | 0.0009 | - |
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+ | 0.4892 | 930 | 0.0019 | - |
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+ | 0.5050 | 960 | 0.0022 | - |
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+ | 0.5471 | 1040 | 0.0004 | - |
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+ | 0.5523 | 1050 | 0.1705 | - |
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+ | 0.5576 | 1060 | 0.0006 | - |
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+ | 0.5629 | 1070 | 0.0006 | - |
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+ | 0.5681 | 1080 | 0.0007 | - |
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+ | 0.5734 | 1090 | 0.1562 | - |
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+ | 0.5786 | 1100 | 0.0008 | - |
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+ | 0.5839 | 1110 | 0.0016 | - |
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+ | 0.5892 | 1120 | 0.001 | - |
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+ | 0.5944 | 1130 | 0.0003 | - |
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+ | 0.5997 | 1140 | 0.0077 | - |
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+ | 0.6049 | 1150 | 0.0006 | - |
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+ | 0.6102 | 1160 | 0.0008 | - |
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+ | 0.6155 | 1170 | 0.0006 | - |
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+ | 0.6207 | 1180 | 0.0007 | - |
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+ | 0.6260 | 1190 | 0.1438 | - |
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+ | 0.6312 | 1200 | 0.0008 | - |
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+ | 0.6365 | 1210 | 0.0012 | - |
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+ | 0.6418 | 1220 | 0.0005 | - |
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+ | 0.6470 | 1230 | 0.0017 | - |
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+ | 0.6523 | 1240 | 0.0007 | - |
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+ | 0.6575 | 1250 | 0.0004 | - |
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+ | 0.6628 | 1260 | 0.0066 | - |
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+ | 0.6681 | 1270 | 0.0004 | - |
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+ | 0.6733 | 1280 | 0.0002 | - |
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+ | 0.6786 | 1290 | 0.1272 | - |
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+ | 0.6839 | 1300 | 0.0019 | - |
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+ | 0.6891 | 1310 | 0.0014 | - |
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+ | 0.6944 | 1320 | 0.0003 | - |
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+ | 0.6996 | 1330 | 0.0007 | - |
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+ | 0.7049 | 1340 | 0.0003 | - |
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+ | 0.7102 | 1350 | 0.0008 | - |
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+ | 0.7154 | 1360 | 0.0005 | - |
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+ | 0.7207 | 1370 | 0.126 | - |
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+ | 0.7259 | 1380 | 0.0003 | - |
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+ | 0.7312 | 1390 | 0.0013 | - |
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+ | 0.7417 | 1410 | 0.0003 | - |
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+ | 0.7628 | 1450 | 0.0009 | - |
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+ | 0.7680 | 1460 | 0.0008 | - |
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+ | 0.7733 | 1470 | 0.0002 | - |
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+ | 0.7838 | 1490 | 0.0007 | - |
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+ | 0.7891 | 1500 | 0.0064 | - |
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+ | 0.7943 | 1510 | 0.0004 | - |
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+ | 0.7996 | 1520 | 0.0006 | - |
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+ | 0.8417 | 1600 | 0.0002 | - |
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+ | 0.8522 | 1620 | 0.0002 | - |
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+ | 0.8574 | 1630 | 0.0005 | - |
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+ | 0.8627 | 1640 | 0.0035 | - |
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+ | 0.8680 | 1650 | 0.0004 | - |
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+ | 0.8732 | 1660 | 0.0025 | - |
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+ | 0.8785 | 1670 | 0.0005 | - |
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+ | 0.8837 | 1680 | 0.0021 | - |
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+ | 0.8890 | 1690 | 0.0003 | - |
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+ | 0.8943 | 1700 | 0.0018 | - |
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+ | 0.8995 | 1710 | 0.0004 | - |
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+ | 0.9048 | 1720 | 0.0002 | - |
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+ | 0.9100 | 1730 | 0.0003 | - |
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+ | 0.9469 | 1800 | 0.0006 | - |
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+ | 0.9521 | 1810 | 0.0007 | - |
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+ | 0.9574 | 1820 | 0.001 | - |
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+ | 0.9627 | 1830 | 0.0003 | - |
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+ | 0.9679 | 1840 | 0.0009 | - |
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+ | 0.9732 | 1850 | 0.0001 | - |
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+ | 0.9784 | 1860 | 0.0006 | - |
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+ | 0.9837 | 1870 | 0.0002 | - |
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+ | 0.9890 | 1880 | 0.0003 | - |
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+ | 0.9942 | 1890 | 0.0004 | - |
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+ | 0.9995 | 1900 | 0.0009 | - |
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+ | 1.0 | 1901 | - | 0.0347 |
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+ | 1.0047 | 1910 | 0.0004 | - |
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+ | 1.0100 | 1920 | 0.0004 | - |
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+ | 1.0153 | 1930 | 0.0005 | - |
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+ | 1.0205 | 1940 | 0.0007 | - |
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+ | 1.0258 | 1950 | 0.0085 | - |
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+ | 1.0310 | 1960 | 0.0003 | - |
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+ | 1.0363 | 1970 | 0.0003 | - |
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+ | 1.0416 | 1980 | 0.0002 | - |
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+ | 1.0468 | 1990 | 0.0009 | - |
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+ | 1.0521 | 2000 | 0.0002 | - |
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+ | 1.0573 | 2010 | 0.0059 | - |
363
+ | 1.0626 | 2020 | 0.0007 | - |
364
+ | 1.0679 | 2030 | 0.0008 | - |
365
+ | 1.0731 | 2040 | 0.0002 | - |
366
+ | 1.0784 | 2050 | 0.0002 | - |
367
+ | 1.0836 | 2060 | 0.0003 | - |
368
+ | 1.0889 | 2070 | 0.0003 | - |
369
+ | 1.0942 | 2080 | 0.0002 | - |
370
+ | 1.0994 | 2090 | 0.0003 | - |
371
+ | 1.1047 | 2100 | 0.0002 | - |
372
+ | 1.1099 | 2110 | 0.0065 | - |
373
+ | 1.1152 | 2120 | 0.0006 | - |
374
+ | 1.1205 | 2130 | 0.0004 | - |
375
+ | 1.1257 | 2140 | 0.0035 | - |
376
+ | 1.1310 | 2150 | 0.0003 | - |
377
+ | 1.1362 | 2160 | 0.0002 | - |
378
+ | 1.1415 | 2170 | 0.0002 | - |
379
+ | 1.1468 | 2180 | 0.0002 | - |
380
+ | 1.1520 | 2190 | 0.001 | - |
381
+ | 1.1573 | 2200 | 0.0003 | - |
382
+ | 1.1625 | 2210 | 0.0002 | - |
383
+ | 1.1678 | 2220 | 0.0002 | - |
384
+ | 1.1731 | 2230 | 0.0002 | - |
385
+ | 1.1783 | 2240 | 0.0003 | - |
386
+ | 1.1836 | 2250 | 0.0002 | - |
387
+ | 1.1888 | 2260 | 0.0008 | - |
388
+ | 1.1941 | 2270 | 0.0002 | - |
389
+ | 1.1994 | 2280 | 0.0018 | - |
390
+ | 1.2046 | 2290 | 0.0001 | - |
391
+ | 1.2099 | 2300 | 0.0002 | - |
392
+ | 1.2151 | 2310 | 0.0005 | - |
393
+ | 1.2204 | 2320 | 0.0008 | - |
394
+ | 1.2257 | 2330 | 0.0002 | - |
395
+ | 1.2309 | 2340 | 0.0003 | - |
396
+ | 1.2362 | 2350 | 0.0002 | - |
397
+ | 1.2415 | 2360 | 0.0003 | - |
398
+ | 1.2467 | 2370 | 0.0001 | - |
399
+ | 1.2520 | 2380 | 0.0002 | - |
400
+ | 1.2572 | 2390 | 0.0002 | - |
401
+ | 1.2625 | 2400 | 0.0002 | - |
402
+ | 1.2678 | 2410 | 0.0003 | - |
403
+ | 1.2730 | 2420 | 0.0002 | - |
404
+ | 1.2783 | 2430 | 0.0002 | - |
405
+ | 1.2835 | 2440 | 0.0002 | - |
406
+ | 1.2888 | 2450 | 0.0003 | - |
407
+ | 1.2941 | 2460 | 0.0004 | - |
408
+ | 1.2993 | 2470 | 0.0002 | - |
409
+ | 1.3046 | 2480 | 0.0002 | - |
410
+ | 1.3098 | 2490 | 0.0006 | - |
411
+ | 1.3151 | 2500 | 0.0002 | - |
412
+ | 1.3204 | 2510 | 0.0002 | - |
413
+ | 1.3256 | 2520 | 0.0001 | - |
414
+ | 1.3309 | 2530 | 0.0037 | - |
415
+ | 1.3361 | 2540 | 0.0004 | - |
416
+ | 1.3414 | 2550 | 0.0003 | - |
417
+ | 1.3467 | 2560 | 0.0001 | - |
418
+ | 1.3519 | 2570 | 0.0001 | - |
419
+ | 1.3572 | 2580 | 0.0003 | - |
420
+ | 1.3624 | 2590 | 0.0002 | - |
421
+ | 1.3677 | 2600 | 0.0003 | - |
422
+ | 1.3730 | 2610 | 0.0003 | - |
423
+ | 1.3782 | 2620 | 0.0003 | - |
424
+ | 1.3835 | 2630 | 0.0003 | - |
425
+ | 1.3887 | 2640 | 0.0002 | - |
426
+ | 1.3940 | 2650 | 0.0034 | - |
427
+ | 1.3993 | 2660 | 0.0002 | - |
428
+ | 1.4045 | 2670 | 0.0004 | - |
429
+ | 1.4098 | 2680 | 0.0004 | - |
430
+ | 1.4150 | 2690 | 0.0003 | - |
431
+ | 1.4203 | 2700 | 0.0003 | - |
432
+ | 1.4256 | 2710 | 0.0007 | - |
433
+ | 1.4308 | 2720 | 0.0002 | - |
434
+ | 1.4361 | 2730 | 0.0004 | - |
435
+ | 1.4413 | 2740 | 0.0004 | - |
436
+ | 1.4466 | 2750 | 0.0005 | - |
437
+ | 1.4519 | 2760 | 0.0003 | - |
438
+ | 1.4571 | 2770 | 0.0003 | - |
439
+ | 1.4624 | 2780 | 0.0005 | - |
440
+ | 1.4676 | 2790 | 0.0015 | - |
441
+ | 1.4729 | 2800 | 0.0005 | - |
442
+ | 1.4782 | 2810 | 0.0003 | - |
443
+ | 1.4834 | 2820 | 0.0003 | - |
444
+ | 1.4887 | 2830 | 0.0002 | - |
445
+ | 1.4940 | 2840 | 0.0003 | - |
446
+ | 1.4992 | 2850 | 0.0004 | - |
447
+ | 1.5045 | 2860 | 0.0025 | - |
448
+ | 1.5097 | 2870 | 0.0001 | - |
449
+ | 1.5150 | 2880 | 0.0002 | - |
450
+ | 1.5203 | 2890 | 0.0004 | - |
451
+ | 1.5255 | 2900 | 0.0001 | - |
452
+ | 1.5308 | 2910 | 0.0003 | - |
453
+ | 1.5360 | 2920 | 0.0006 | - |
454
+ | 1.5413 | 2930 | 0.0001 | - |
455
+ | 1.5466 | 2940 | 0.0001 | - |
456
+ | 1.5518 | 2950 | 0.0004 | - |
457
+ | 1.5571 | 2960 | 0.0002 | - |
458
+ | 1.5623 | 2970 | 0.0006 | - |
459
+ | 1.5676 | 2980 | 0.0003 | - |
460
+ | 1.5729 | 2990 | 0.001 | - |
461
+ | 1.5781 | 3000 | 0.0003 | - |
462
+ | 1.5834 | 3010 | 0.0002 | - |
463
+ | 1.5886 | 3020 | 0.0003 | - |
464
+ | 1.5939 | 3030 | 0.0002 | - |
465
+ | 1.5992 | 3040 | 0.0001 | - |
466
+ | 1.6044 | 3050 | 0.0002 | - |
467
+ | 1.6097 | 3060 | 0.0002 | - |
468
+ | 1.6149 | 3070 | 0.0002 | - |
469
+ | 1.6202 | 3080 | 0.0001 | - |
470
+ | 1.6255 | 3090 | 0.0002 | - |
471
+ | 1.6307 | 3100 | 0.0002 | - |
472
+ | 1.6360 | 3110 | 0.0001 | - |
473
+ | 1.6412 | 3120 | 0.0001 | - |
474
+ | 1.6465 | 3130 | 0.0002 | - |
475
+ | 1.6518 | 3140 | 0.0003 | - |
476
+ | 1.6570 | 3150 | 0.0002 | - |
477
+ | 1.6623 | 3160 | 0.0002 | - |
478
+ | 1.6675 | 3170 | 0.0001 | - |
479
+ | 1.6728 | 3180 | 0.0002 | - |
480
+ | 1.6781 | 3190 | 0.0002 | - |
481
+ | 1.6833 | 3200 | 0.0008 | - |
482
+ | 1.6886 | 3210 | 0.0002 | - |
483
+ | 1.6938 | 3220 | 0.0003 | - |
484
+ | 1.6991 | 3230 | 0.0001 | - |
485
+ | 1.7044 | 3240 | 0.0001 | - |
486
+ | 1.7096 | 3250 | 0.0001 | - |
487
+ | 1.7149 | 3260 | 0.0002 | - |
488
+ | 1.7201 | 3270 | 0.0003 | - |
489
+ | 1.7254 | 3280 | 0.0001 | - |
490
+ | 1.7307 | 3290 | 0.0003 | - |
491
+ | 1.7359 | 3300 | 0.0001 | - |
492
+ | 1.7412 | 3310 | 0.0003 | - |
493
+ | 1.7464 | 3320 | 0.0002 | - |
494
+ | 1.7517 | 3330 | 0.0002 | - |
495
+ | 1.7570 | 3340 | 0.0001 | - |
496
+ | 1.7622 | 3350 | 0.0002 | - |
497
+ | 1.7675 | 3360 | 0.0001 | - |
498
+ | 1.7728 | 3370 | 0.0005 | - |
499
+ | 1.7780 | 3380 | 0.0001 | - |
500
+ | 1.7833 | 3390 | 0.0003 | - |
501
+ | 1.7885 | 3400 | 0.0002 | - |
502
+ | 1.7938 | 3410 | 0.0003 | - |
503
+ | 1.7991 | 3420 | 0.0002 | - |
504
+ | 1.8043 | 3430 | 0.0002 | - |
505
+ | 1.8096 | 3440 | 0.0009 | - |
506
+ | 1.8148 | 3450 | 0.0001 | - |
507
+ | 1.8201 | 3460 | 0.0005 | - |
508
+ | 1.8254 | 3470 | 0.0002 | - |
509
+ | 1.8306 | 3480 | 0.0004 | - |
510
+ | 1.8359 | 3490 | 0.0002 | - |
511
+ | 1.8411 | 3500 | 0.0001 | - |
512
+ | 1.8464 | 3510 | 0.0001 | - |
513
+ | 1.8517 | 3520 | 0.0003 | - |
514
+ | 1.8569 | 3530 | 0.0001 | - |
515
+ | 1.8622 | 3540 | 0.0002 | - |
516
+ | 1.8674 | 3550 | 0.0002 | - |
517
+ | 1.8727 | 3560 | 0.0011 | - |
518
+ | 1.8780 | 3570 | 0.0003 | - |
519
+ | 1.8832 | 3580 | 0.0003 | - |
520
+ | 1.8885 | 3590 | 0.0002 | - |
521
+ | 1.8937 | 3600 | 0.0001 | - |
522
+ | 1.8990 | 3610 | 0.0001 | - |
523
+ | 1.9043 | 3620 | 0.0002 | - |
524
+ | 1.9095 | 3630 | 0.0001 | - |
525
+ | 1.9148 | 3640 | 0.0002 | - |
526
+ | 1.9200 | 3650 | 0.0002 | - |
527
+ | 1.9253 | 3660 | 0.0002 | - |
528
+ | 1.9306 | 3670 | 0.0002 | - |
529
+ | 1.9358 | 3680 | 0.0001 | - |
530
+ | 1.9411 | 3690 | 0.0002 | - |
531
+ | 1.9463 | 3700 | 0.0003 | - |
532
+ | 1.9516 | 3710 | 0.0006 | - |
533
+ | 1.9569 | 3720 | 0.0004 | - |
534
+ | 1.9621 | 3730 | 0.0001 | - |
535
+ | 1.9674 | 3740 | 0.0002 | - |
536
+ | 1.9726 | 3750 | 0.0004 | - |
537
+ | 1.9779 | 3760 | 0.0002 | - |
538
+ | 1.9832 | 3770 | 0.0004 | - |
539
+ | 1.9884 | 3780 | 0.0003 | - |
540
+ | 1.9937 | 3790 | 0.0002 | - |
541
+ | 1.9989 | 3800 | 0.0002 | - |
542
+ | 2.0 | 3802 | - | 0.0333 |
543
+ | 2.0042 | 3810 | 0.0001 | - |
544
+ | 2.0095 | 3820 | 0.0002 | - |
545
+ | 2.0147 | 3830 | 0.0004 | - |
546
+ | 2.0200 | 3840 | 0.0005 | - |
547
+ | 2.0252 | 3850 | 0.0002 | - |
548
+ | 2.0305 | 3860 | 0.0001 | - |
549
+ | 2.0358 | 3870 | 0.0005 | - |
550
+ | 2.0410 | 3880 | 0.0002 | - |
551
+ | 2.0463 | 3890 | 0.0002 | - |
552
+ | 2.0516 | 3900 | 0.0002 | - |
553
+ | 2.0568 | 3910 | 0.0003 | - |
554
+ | 2.0621 | 3920 | 0.0002 | - |
555
+ | 2.0673 | 3930 | 0.0005 | - |
556
+ | 2.0726 | 3940 | 0.0002 | - |
557
+ | 2.0779 | 3950 | 0.0001 | - |
558
+ | 2.0831 | 3960 | 0.0001 | - |
559
+ | 2.0884 | 3970 | 0.0003 | - |
560
+ | 2.0936 | 3980 | 0.0001 | - |
561
+ | 2.0989 | 3990 | 0.0002 | - |
562
+ | 2.1042 | 4000 | 0.0001 | - |
563
+ | 2.1094 | 4010 | 0.0001 | - |
564
+ | 2.1147 | 4020 | 0.0001 | - |
565
+ | 2.1199 | 4030 | 0.0004 | - |
566
+ | 2.1252 | 4040 | 0.0002 | - |
567
+ | 2.1305 | 4050 | 0.0003 | - |
568
+ | 2.1357 | 4060 | 0.0002 | - |
569
+ | 2.1410 | 4070 | 0.0001 | - |
570
+ | 2.1462 | 4080 | 0.0001 | - |
571
+ | 2.1515 | 4090 | 0.0001 | - |
572
+ | 2.1568 | 4100 | 0.0001 | - |
573
+ | 2.1620 | 4110 | 0.0001 | - |
574
+ | 2.1673 | 4120 | 0.0001 | - |
575
+ | 2.1725 | 4130 | 0.0001 | - |
576
+ | 2.1778 | 4140 | 0.0001 | - |
577
+ | 2.1831 | 4150 | 0.0009 | - |
578
+ | 2.1883 | 4160 | 0.0001 | - |
579
+ | 2.1936 | 4170 | 0.0003 | - |
580
+ | 2.1988 | 4180 | 0.0001 | - |
581
+ | 2.2041 | 4190 | 0.0002 | - |
582
+ | 2.2094 | 4200 | 0.0003 | - |
583
+ | 2.2146 | 4210 | 0.0008 | - |
584
+ | 2.2199 | 4220 | 0.0002 | - |
585
+ | 2.2251 | 4230 | 0.0004 | - |
586
+ | 2.2304 | 4240 | 0.0002 | - |
587
+ | 2.2357 | 4250 | 0.0001 | - |
588
+ | 2.2409 | 4260 | 0.0004 | - |
589
+ | 2.2462 | 4270 | 0.0001 | - |
590
+ | 2.2514 | 4280 | 0.0001 | - |
591
+ | 2.2567 | 4290 | 0.0001 | - |
592
+ | 2.2620 | 4300 | 0.0001 | - |
593
+ | 2.2672 | 4310 | 0.0002 | - |
594
+ | 2.2725 | 4320 | 0.0002 | - |
595
+ | 2.2777 | 4330 | 0.0002 | - |
596
+ | 2.2830 | 4340 | 0.0002 | - |
597
+ | 2.2883 | 4350 | 0.0001 | - |
598
+ | 2.2935 | 4360 | 0.0001 | - |
599
+ | 2.2988 | 4370 | 0.0001 | - |
600
+ | 2.3041 | 4380 | 0.0004 | - |
601
+ | 2.3093 | 4390 | 0.0002 | - |
602
+ | 2.3146 | 4400 | 0.0001 | - |
603
+ | 2.3198 | 4410 | 0.0004 | - |
604
+ | 2.3251 | 4420 | 0.0001 | - |
605
+ | 2.3304 | 4430 | 0.0001 | - |
606
+ | 2.3356 | 4440 | 0.0001 | - |
607
+ | 2.3409 | 4450 | 0.0001 | - |
608
+ | 2.3461 | 4460 | 0.0001 | - |
609
+ | 2.3514 | 4470 | 0.0002 | - |
610
+ | 2.3567 | 4480 | 0.0004 | - |
611
+ | 2.3619 | 4490 | 0.0003 | - |
612
+ | 2.3672 | 4500 | 0.0002 | - |
613
+ | 2.3724 | 4510 | 0.0001 | - |
614
+ | 2.3777 | 4520 | 0.0001 | - |
615
+ | 2.3830 | 4530 | 0.0001 | - |
616
+ | 2.3882 | 4540 | 0.0001 | - |
617
+ | 2.3935 | 4550 | 0.0001 | - |
618
+ | 2.3987 | 4560 | 0.0002 | - |
619
+ | 2.4040 | 4570 | 0.0001 | - |
620
+ | 2.4093 | 4580 | 0.0001 | - |
621
+ | 2.4145 | 4590 | 0.0001 | - |
622
+ | 2.4198 | 4600 | 0.0001 | - |
623
+ | 2.4250 | 4610 | 0.0001 | - |
624
+ | 2.4303 | 4620 | 0.0008 | - |
625
+ | 2.4356 | 4630 | 0.0001 | - |
626
+ | 2.4408 | 4640 | 0.0002 | - |
627
+ | 2.4461 | 4650 | 0.0001 | - |
628
+ | 2.4513 | 4660 | 0.0001 | - |
629
+ | 2.4566 | 4670 | 0.0001 | - |
630
+ | 2.4619 | 4680 | 0.0001 | - |
631
+ | 2.4671 | 4690 | 0.0001 | - |
632
+ | 2.4724 | 4700 | 0.0001 | - |
633
+ | 2.4776 | 4710 | 0.0001 | - |
634
+ | 2.4829 | 4720 | 0.0001 | - |
635
+ | 2.4882 | 4730 | 0.0002 | - |
636
+ | 2.4934 | 4740 | 0.0001 | - |
637
+ | 2.4987 | 4750 | 0.0001 | - |
638
+ | 2.5039 | 4760 | 0.0008 | - |
639
+ | 2.5092 | 4770 | 0.0002 | - |
640
+ | 2.5145 | 4780 | 0.0001 | - |
641
+ | 2.5197 | 4790 | 0.0001 | - |
642
+ | 2.5250 | 4800 | 0.0007 | - |
643
+ | 2.5302 | 4810 | 0.0003 | - |
644
+ | 2.5355 | 4820 | 0.0001 | - |
645
+ | 2.5408 | 4830 | 0.0001 | - |
646
+ | 2.5460 | 4840 | 0.0001 | - |
647
+ | 2.5513 | 4850 | 0.0003 | - |
648
+ | 2.5565 | 4860 | 0.0001 | - |
649
+ | 2.5618 | 4870 | 0.0001 | - |
650
+ | 2.5671 | 4880 | 0.0002 | - |
651
+ | 2.5723 | 4890 | 0.0001 | - |
652
+ | 2.5776 | 4900 | 0.0001 | - |
653
+ | 2.5829 | 4910 | 0.0003 | - |
654
+ | 2.5881 | 4920 | 0.0001 | - |
655
+ | 2.5934 | 4930 | 0.0002 | - |
656
+ | 2.5986 | 4940 | 0.0003 | - |
657
+ | 2.6039 | 4950 | 0.0001 | - |
658
+ | 2.6092 | 4960 | 0.0002 | - |
659
+ | 2.6144 | 4970 | 0.0001 | - |
660
+ | 2.6197 | 4980 | 0.0002 | - |
661
+ | 2.6249 | 4990 | 0.0002 | - |
662
+ | 2.6302 | 5000 | 0.0002 | - |
663
+ | 2.6355 | 5010 | 0.0004 | - |
664
+ | 2.6407 | 5020 | 0.0001 | - |
665
+ | 2.6460 | 5030 | 0.0001 | - |
666
+ | 2.6512 | 5040 | 0.0004 | - |
667
+ | 2.6565 | 5050 | 0.0001 | - |
668
+ | 2.6618 | 5060 | 0.0002 | - |
669
+ | 2.6670 | 5070 | 0.0014 | - |
670
+ | 2.6723 | 5080 | 0.0003 | - |
671
+ | 2.6775 | 5090 | 0.0001 | - |
672
+ | 2.6828 | 5100 | 0.0003 | - |
673
+ | 2.6881 | 5110 | 0.0001 | - |
674
+ | 2.6933 | 5120 | 0.0001 | - |
675
+ | 2.6986 | 5130 | 0.0009 | - |
676
+ | 2.7038 | 5140 | 0.0002 | - |
677
+ | 2.7091 | 5150 | 0.0003 | - |
678
+ | 2.7144 | 5160 | 0.0001 | - |
679
+ | 2.7196 | 5170 | 0.0001 | - |
680
+ | 2.7249 | 5180 | 0.0002 | - |
681
+ | 2.7301 | 5190 | 0.0001 | - |
682
+ | 2.7354 | 5200 | 0.0001 | - |
683
+ | 2.7407 | 5210 | 0.0001 | - |
684
+ | 2.7459 | 5220 | 0.0002 | - |
685
+ | 2.7512 | 5230 | 0.0004 | - |
686
+ | 2.7564 | 5240 | 0.0001 | - |
687
+ | 2.7617 | 5250 | 0.0001 | - |
688
+ | 2.7670 | 5260 | 0.0004 | - |
689
+ | 2.7722 | 5270 | 0.0003 | - |
690
+ | 2.7775 | 5280 | 0.0002 | - |
691
+ | 2.7827 | 5290 | 0.0002 | - |
692
+ | 2.7880 | 5300 | 0.0001 | - |
693
+ | 2.7933 | 5310 | 0.0003 | - |
694
+ | 2.7985 | 5320 | 0.0001 | - |
695
+ | 2.8038 | 5330 | 0.0005 | - |
696
+ | 2.8090 | 5340 | 0.0001 | - |
697
+ | 2.8143 | 5350 | 0.0001 | - |
698
+ | 2.8196 | 5360 | 0.0001 | - |
699
+ | 2.8248 | 5370 | 0.0001 | - |
700
+ | 2.8301 | 5380 | 0.0003 | - |
701
+ | 2.8353 | 5390 | 0.0001 | - |
702
+ | 2.8406 | 5400 | 0.0008 | - |
703
+ | 2.8459 | 5410 | 0.0001 | - |
704
+ | 2.8511 | 5420 | 0.0001 | - |
705
+ | 2.8564 | 5430 | 0.0001 | - |
706
+ | 2.8617 | 5440 | 0.0002 | - |
707
+ | 2.8669 | 5450 | 0.0001 | - |
708
+ | 2.8722 | 5460 | 0.0004 | - |
709
+ | 2.8774 | 5470 | 0.0001 | - |
710
+ | 2.8827 | 5480 | 0.0001 | - |
711
+ | 2.8880 | 5490 | 0.0002 | - |
712
+ | 2.8932 | 5500 | 0.0001 | - |
713
+ | 2.8985 | 5510 | 0.0001 | - |
714
+ | 2.9037 | 5520 | 0.0001 | - |
715
+ | 2.9090 | 5530 | 0.0002 | - |
716
+ | 2.9143 | 5540 | 0.0002 | - |
717
+ | 2.9195 | 5550 | 0.0001 | - |
718
+ | 2.9248 | 5560 | 0.0001 | - |
719
+ | 2.9300 | 5570 | 0.0005 | - |
720
+ | 2.9353 | 5580 | 0.0002 | - |
721
+ | 2.9406 | 5590 | 0.0001 | - |
722
+ | 2.9458 | 5600 | 0.0001 | - |
723
+ | 2.9511 | 5610 | 0.0003 | - |
724
+ | 2.9563 | 5620 | 0.0001 | - |
725
+ | 2.9616 | 5630 | 0.0001 | - |
726
+ | 2.9669 | 5640 | 0.0001 | - |
727
+ | 2.9721 | 5650 | 0.0006 | - |
728
+ | 2.9774 | 5660 | 0.0001 | - |
729
+ | 2.9826 | 5670 | 0.0001 | - |
730
+ | 2.9879 | 5680 | 0.0001 | - |
731
+ | 2.9932 | 5690 | 0.0001 | - |
732
+ | 2.9984 | 5700 | 0.0001 | - |
733
+ | 3.0 | 5703 | - | 0.0349 |
734
+ | 3.0037 | 5710 | 0.0001 | - |
735
+ | 3.0089 | 5720 | 0.0001 | - |
736
+ | 3.0142 | 5730 | 0.0002 | - |
737
+ | 3.0195 | 5740 | 0.0001 | - |
738
+ | 3.0247 | 5750 | 0.0002 | - |
739
+ | 3.0300 | 5760 | 0.0001 | - |
740
+ | 3.0352 | 5770 | 0.0008 | - |
741
+ | 3.0405 | 5780 | 0.0004 | - |
742
+ | 3.0458 | 5790 | 0.0003 | - |
743
+ | 3.0510 | 5800 | 0.0001 | - |
744
+ | 3.0563 | 5810 | 0.0001 | - |
745
+ | 3.0615 | 5820 | 0.0006 | - |
746
+ | 3.0668 | 5830 | 0.0002 | - |
747
+ | 3.0721 | 5840 | 0.0001 | - |
748
+ | 3.0773 | 5850 | 0.0002 | - |
749
+ | 3.0826 | 5860 | 0.0002 | - |
750
+ | 3.0878 | 5870 | 0.0002 | - |
751
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+ | **4.0** | **7604** | **-** | **0.0319** |
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+
926
+ * The bold row denotes the saved checkpoint.
927
+ ### Framework Versions
928
+ - Python: 3.10.12
929
+ - SetFit: 1.0.3
930
+ - Sentence Transformers: 3.0.1
931
+ - Transformers: 4.37.0
932
+ - PyTorch: 2.5.1+cu121
933
+ - Datasets: 3.1.0
934
+ - Tokenizers: 0.15.2
935
+
936
+ ## Citation
937
+
938
+ ### BibTeX
939
+ ```bibtex
940
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
941
+ doi = {10.48550/ARXIV.2209.11055},
942
+ url = {https://arxiv.org/abs/2209.11055},
943
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
944
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
945
+ title = {Efficient Few-Shot Learning Without Prompts},
946
+ publisher = {arXiv},
947
+ year = {2022},
948
+ copyright = {Creative Commons Attribution 4.0 International}
949
+ }
950
+ ```
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+
952
+ <!--
953
+ ## Glossary
954
+
955
+ *Clearly define terms in order to be accessible across audiences.*
956
+ -->
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+
958
+ <!--
959
+ ## Model Card Authors
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+
961
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
962
+ -->
963
+
964
+ <!--
965
+ ## Model Card Contact
966
+
967
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
968
+ -->
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