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Shankhdhar
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classifier_woog_base_oos_combined

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

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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +390 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +9 -0
  5. config_setfit.json +11 -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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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ base_model: sentence-transformers/paraphrase-mpnet-base-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: Do you have the Nike Blazer Mid sacai Snow Beach in size 9?
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+ - text: How can I adapt K-beauty routines for dry weather?
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+ - text: I like to listen to classical music
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+ - text: If this product is for weight management, what is the sub-category?
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+ - text: How long does it take to receive a refund after returning a product?
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+ inference: true
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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: 0.8711340206185567
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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 [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:** 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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+ | product policy | <ul><li>'Do you offer a gift wrapping service for sneakers?'</li><li>'What are the consequences if my account is suspended or terminated for any reason?'</li><li>'Do you share my personal information with third parties?'</li></ul> |
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+ | general faq | <ul><li>'Can you explain why Mashru silk is considered more comfortable to wear compared to pure silk sarees?'</li><li>'What are some tips for maximizing the antioxidant content when brewing green tea?'</li><li>'Can you recommend K-beauty products for hot and humid climates?'</li></ul> |
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+ | product discoverability | <ul><li>'Are there any sarees with Kadwa Weave technique?'</li><li>'cookie boxes with dividers'</li><li>'Are there any products for dry skin?'</li></ul> |
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+ | Out of Scope | <ul><li>'Is this website secure?'</li><li>'How do you handle intellectual property disputes?'</li><li>'Do you know how to play the piano?'</li></ul> |
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+ | order tracking | <ul><li>'I want to deliver candle supplies to Jaipur, how many days will it take to deliver?'</li><li>'I want to deliver bags to Pune, how many days will it take to deliver?'</li><li>'I need to change the delivery address for my recent order, how can I do that?'</li></ul> |
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+ | product faq | <ul><li>'Does this product help with dark spots?'</li><li>'3. Is this product currently in stock?'</li><li>'Is the product in stock?'</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** | 0.8711 |
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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("setfit_model_id")
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+ # Run inference
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+ preds = model("I like to listen to classical music")
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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 | 4 | 10.66 | 28 |
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+
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+ | Label | Training Sample Count |
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+ |:------------------------|:----------------------|
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+ | Out of Scope | 50 |
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+ | general faq | 50 |
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+ | order tracking | 50 |
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+ | product discoverability | 50 |
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+ | product faq | 50 |
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+ | product policy | 50 |
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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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+ - 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 | 0.2592 | - |
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+ | 0.0107 | 50 | 0.2424 | - |
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+ | 0.0213 | 100 | 0.1506 | - |
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+ | 0.0320 | 150 | 0.222 | - |
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+ | 0.0427 | 200 | 0.1227 | - |
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+ | 0.0533 | 250 | 0.1801 | - |
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+ | 0.0640 | 300 | 0.1111 | - |
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+ | 0.0747 | 350 | 0.0346 | - |
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+ | 0.0853 | 400 | 0.0313 | - |
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+ | 0.0960 | 450 | 0.0048 | - |
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+ | 0.1067 | 500 | 0.0023 | - |
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+ | 0.1173 | 550 | 0.0018 | - |
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+ | 0.1280 | 600 | 0.0133 | - |
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+ | 0.1387 | 650 | 0.0008 | - |
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+ | 0.1493 | 700 | 0.0006 | - |
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+ | 0.1600 | 750 | 0.0005 | - |
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+ | 0.1706 | 800 | 0.0008 | - |
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+ | 0.1813 | 850 | 0.0007 | - |
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+ | 0.1920 | 900 | 0.0006 | - |
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+ | 0.2026 | 950 | 0.0006 | - |
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+ | 0.2133 | 1000 | 0.0003 | - |
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+ | 0.2240 | 1050 | 0.0026 | - |
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+ | 0.2346 | 1100 | 0.0004 | - |
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+ | 0.2453 | 1150 | 0.0004 | - |
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+ | 0.2560 | 1200 | 0.0004 | - |
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+ | 0.2666 | 1250 | 0.0005 | - |
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+ | 0.2773 | 1300 | 0.0005 | - |
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+ | 0.2880 | 1350 | 0.0003 | - |
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+ | 0.2986 | 1400 | 0.0001 | - |
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+ | 0.3093 | 1450 | 0.0001 | - |
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+ | 0.3200 | 1500 | 0.0002 | - |
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+ | 0.3306 | 1550 | 0.0002 | - |
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+ | 0.3413 | 1600 | 0.0002 | - |
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+ | 0.3520 | 1650 | 0.0001 | - |
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+ | 0.3626 | 1700 | 0.0004 | - |
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+ | 0.3733 | 1750 | 0.0002 | - |
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+ | 0.3840 | 1800 | 0.0005 | - |
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+ | 0.3946 | 1850 | 0.0002 | - |
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+ | 0.4053 | 1900 | 0.0002 | - |
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+ | 0.4160 | 1950 | 0.0001 | - |
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+ | 0.4266 | 2000 | 0.0001 | - |
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+ | 0.4373 | 2050 | 0.0001 | - |
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+ | 0.4480 | 2100 | 0.0001 | - |
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+ | 0.4586 | 2150 | 0.0001 | - |
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+ | 0.4693 | 2200 | 0.0002 | - |
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+ | 0.4799 | 2250 | 0.0048 | - |
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+ | 0.4906 | 2300 | 0.0001 | - |
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+ | 0.5013 | 2350 | 0.001 | - |
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+ | 0.5119 | 2400 | 0.0002 | - |
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+ | 0.5226 | 2450 | 0.0002 | - |
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+ | 0.5333 | 2500 | 0.0001 | - |
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+ | 0.5439 | 2550 | 0.0001 | - |
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+ | 0.5546 | 2600 | 0.0001 | - |
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+ | 0.5653 | 2650 | 0.0001 | - |
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+ | 0.5759 | 2700 | 0.0001 | - |
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+ | 0.5866 | 2750 | 0.0001 | - |
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+ | 0.5973 | 2800 | 0.0001 | - |
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+ | 0.6079 | 2850 | 0.0001 | - |
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+ | 0.6186 | 2900 | 0.0001 | - |
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+ | 0.6293 | 2950 | 0.0001 | - |
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+ | 0.6399 | 3000 | 0.0001 | - |
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+ | 0.6506 | 3050 | 0.0001 | - |
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+ | 0.6613 | 3100 | 0.0001 | - |
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+ | 0.6719 | 3150 | 0.0001 | - |
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+ | 0.6826 | 3200 | 0.0001 | - |
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+ | 0.6933 | 3250 | 0.0001 | - |
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+ | 0.7039 | 3300 | 0.0001 | - |
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+ | 0.7146 | 3350 | 0.0001 | - |
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+ | 0.7253 | 3400 | 0.0001 | - |
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+ | 0.7359 | 3450 | 0.0001 | - |
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+ | 0.7466 | 3500 | 0.0001 | - |
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+ | 0.7573 | 3550 | 0.0001 | - |
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+ | 0.7679 | 3600 | 0.0001 | - |
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+ | 0.7786 | 3650 | 0.0001 | - |
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+ | 0.7892 | 3700 | 0.0001 | - |
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+ | 0.7999 | 3750 | 0.0001 | - |
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+ | 0.8106 | 3800 | 0.0001 | - |
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+ | 0.8212 | 3850 | 0.0 | - |
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+ | 0.8319 | 3900 | 0.0001 | - |
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+ | 0.8426 | 3950 | 0.0001 | - |
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+ | 0.8532 | 4000 | 0.0001 | - |
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+ | 0.8639 | 4050 | 0.0001 | - |
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+ | 0.8746 | 4100 | 0.0001 | - |
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+ | 0.8852 | 4150 | 0.0 | - |
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+ | 0.8959 | 4200 | 0.0001 | - |
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+ | 0.9172 | 4300 | 0.0001 | - |
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+ | 0.9599 | 4500 | 0.0001 | - |
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+ | 0.9706 | 4550 | 0.0001 | - |
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+ | 0.9919 | 4650 | 0.0001 | - |
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+ | 1.0026 | 4700 | 0.0 | - |
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+ | 1.0132 | 4750 | 0.0001 | - |
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+ | 1.0346 | 4850 | 0.0001 | - |
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+ | 1.0452 | 4900 | 0.0001 | - |
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+ | 1.0559 | 4950 | 0.0001 | - |
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+ | 1.0666 | 5000 | 0.0 | - |
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+ | 1.0772 | 5050 | 0.0 | - |
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+ | 1.0879 | 5100 | 0.0001 | - |
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+ | 1.0985 | 5150 | 0.0 | - |
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+ | 1.1092 | 5200 | 0.0 | - |
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+ | 1.1199 | 5250 | 0.0 | - |
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+ | 1.1305 | 5300 | 0.0001 | - |
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+ | 1.1412 | 5350 | 0.0001 | - |
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+ | 1.1519 | 5400 | 0.0 | - |
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+ | 1.1625 | 5450 | 0.0001 | - |
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+ | 1.1839 | 5550 | 0.0002 | - |
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+ | 1.1945 | 5600 | 0.0 | - |
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+ | 1.2692 | 5950 | 0.0 | - |
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+ | 1.2905 | 6050 | 0.0 | - |
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+ | 1.3119 | 6150 | 0.0 | - |
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+ | 1.3225 | 6200 | 0.0 | - |
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+ | 1.3332 | 6250 | 0.0 | - |
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+ | 1.3439 | 6300 | 0.0 | - |
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+ | 1.3652 | 6400 | 0.0 | - |
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+ | 1.3759 | 6450 | 0.0 | - |
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+ | 1.3865 | 6500 | 0.0 | - |
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+ | 1.3972 | 6550 | 0.0 | - |
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+ | 1.4078 | 6600 | 0.0 | - |
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+ | 1.4185 | 6650 | 0.0 | - |
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+ | 1.4292 | 6700 | 0.0 | - |
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+ | 1.4398 | 6750 | 0.0 | - |
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+ | 1.4505 | 6800 | 0.0 | - |
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+ | 1.4612 | 6850 | 0.0 | - |
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+ | 1.4718 | 6900 | 0.0001 | - |
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+ | 1.4825 | 6950 | 0.0001 | - |
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+ | 1.4932 | 7000 | 0.0 | - |
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+ | 1.5038 | 7050 | 0.0 | - |
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+ | 1.5145 | 7100 | 0.0001 | - |
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+ | 1.5252 | 7150 | 0.0001 | - |
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+ | 1.5358 | 7200 | 0.0001 | - |
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+ | 1.5465 | 7250 | 0.0001 | - |
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+ | 1.5572 | 7300 | 0.0 | - |
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+ | 1.5678 | 7350 | 0.0 | - |
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+ | 1.5785 | 7400 | 0.0 | - |
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+ | 1.5892 | 7450 | 0.0001 | - |
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+ | 1.5998 | 7500 | 0.0 | - |
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+ | 1.6105 | 7550 | 0.0 | - |
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+ | 1.6212 | 7600 | 0.0 | - |
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+ | 1.6318 | 7650 | 0.0 | - |
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+ | 1.6425 | 7700 | 0.0 | - |
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+ | 1.6532 | 7750 | 0.0 | - |
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+ | 1.6638 | 7800 | 0.0 | - |
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+ | 1.6745 | 7850 | 0.0 | - |
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+ | 1.6852 | 7900 | 0.0 | - |
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+ | 1.6958 | 7950 | 0.0 | - |
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+ | 1.7065 | 8000 | 0.0 | - |
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+ | 1.7172 | 8050 | 0.0 | - |
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+ | 1.7278 | 8100 | 0.0 | - |
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+ | 1.7385 | 8150 | 0.0001 | - |
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+ | 1.7491 | 8200 | 0.0 | - |
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+ | 1.7598 | 8250 | 0.0 | - |
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+ | 1.7705 | 8300 | 0.0 | - |
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+ | 1.7811 | 8350 | 0.0001 | - |
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+ | 1.7918 | 8400 | 0.0 | - |
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+ | 1.8025 | 8450 | 0.0 | - |
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+ | 1.8131 | 8500 | 0.0 | - |
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+ | 1.8238 | 8550 | 0.0 | - |
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+ | 1.8345 | 8600 | 0.0001 | - |
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+ | 1.8451 | 8650 | 0.0 | - |
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+ | 1.8558 | 8700 | 0.0 | - |
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+ | 1.8665 | 8750 | 0.0001 | - |
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+ | 1.8771 | 8800 | 0.0 | - |
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+ | 1.8878 | 8850 | 0.0 | - |
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+ | 1.8985 | 8900 | 0.0 | - |
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+ | 1.9091 | 8950 | 0.0001 | - |
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+ | 1.9198 | 9000 | 0.0 | - |
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+ | 1.9305 | 9050 | 0.0 | - |
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+ | 1.9411 | 9100 | 0.0 | - |
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+ | 1.9518 | 9150 | 0.0 | - |
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+ | 1.9625 | 9200 | 0.0 | - |
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+ | 1.9731 | 9250 | 0.0 | - |
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+ | 1.9838 | 9300 | 0.0 | - |
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+ | 1.9945 | 9350 | 0.0 | - |
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+
349
+ ### Framework Versions
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+ - Python: 3.10.16
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+ - SetFit: 1.0.3
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+ - Sentence Transformers: 2.7.0
353
+ - Transformers: 4.40.2
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+ - PyTorch: 2.2.2
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+ - Datasets: 2.19.1
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+ - Tokenizers: 0.19.1
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+
358
+ ## Citation
359
+
360
+ ### BibTeX
361
+ ```bibtex
362
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
363
+ 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}
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+ }
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+ ```
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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.*
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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
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "_name_or_path": "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_dropout_prob": 0.1,
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