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Vishal24
/
BCG-classifier

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
Vishal24 commited on
Commit
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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +725 -0
  3. config.json +26 -0
  4. config_sentence_transformers.json +10 -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 +37 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +55 -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: Vishal24/bert-1ds-domain
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+ datasets:
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+ - Vishal24/BCG_classifier
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+ library_name: setfit
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+ metrics:
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+ - f1
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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: fair and handsome 100 oil clear face wash
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+ - text: hazelnut
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+ - text: aqualohica body mist
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+ - text: joy body lotion 300 ml
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+ - text: top of browse listings page
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+ inference: true
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+ model-index:
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+ - name: SetFit with Vishal24/bert-1ds-domain
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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: Vishal24/BCG_classifier
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+ type: Vishal24/BCG_classifier
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+ split: test
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+ metrics:
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+ - type: f1
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+ value: 0.9233278955954323
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+ name: F1
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+ ---
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+
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+ # SetFit with Vishal24/bert-1ds-domain
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [Vishal24/BCG_classifier](https://huggingface.co/datasets/Vishal24/BCG_classifier) dataset that can be used for Text Classification. This SetFit model uses [Vishal24/bert-1ds-domain](https://huggingface.co/Vishal24/bert-1ds-domain) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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:** [Vishal24/bert-1ds-domain](https://huggingface.co/Vishal24/bert-1ds-domain)
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+ - **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 2 classes
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+ - **Training Dataset:** [Vishal24/BCG_classifier](https://huggingface.co/datasets/Vishal24/BCG_classifier)
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:------|:-------------------------------------------------------------------------------------------------------------------------------------------|
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+ | 0 | <ul><li>'mois'</li><li>'time skincare soap'</li><li>'paraben free'</li></ul> |
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+ | 1 | <ul><li>'tomato ketchup 1kg flipkart'</li><li>'sunsilk keratin yogurt shampoo lusciously thick long'</li><li>'wow aloevera soap'</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 | F1 |
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+ |:--------|:-------|
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+ | **all** | 0.9233 |
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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("Vishal24/BCG-classifier")
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+ # Run inference
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+ preds = model("hazelnut")
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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 | 3.4474 | 19 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 2252 |
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+ | 1 | 1262 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 2)
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+ - num_epochs: (3, 3)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 20
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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: False
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:------:|:-----:|:-------------:|:---------------:|
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+ | 0.0001 | 1 | 0.2765 | - |
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+ | 0.0057 | 50 | 0.2529 | - |
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+ | 0.0114 | 100 | 0.252 | - |
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+ | 0.0171 | 150 | 0.2657 | - |
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+ | 0.0228 | 200 | 0.2735 | - |
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+ | 0.0285 | 250 | 0.236 | - |
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+ | 0.0341 | 300 | 0.2366 | - |
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+ | 0.0398 | 350 | 0.2316 | - |
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+ | 0.0455 | 400 | 0.185 | - |
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+ | 0.0512 | 450 | 0.1396 | - |
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+ | 0.0569 | 500 | 0.2137 | - |
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+ | 0.0626 | 550 | 0.093 | - |
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+ | 0.0683 | 600 | 0.1219 | - |
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+ | 0.0740 | 650 | 0.0974 | - |
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+ | 0.0797 | 700 | 0.2257 | - |
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+ | 0.0854 | 750 | 0.0951 | - |
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+ | 0.0911 | 800 | 0.0994 | - |
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+ | 0.0968 | 850 | 0.0752 | - |
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+ | 0.1024 | 900 | 0.0848 | - |
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+ | 0.1081 | 950 | 0.015 | - |
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+ | 0.1138 | 1000 | 0.0541 | - |
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+ | 0.1195 | 1050 | 0.0357 | - |
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+ | 0.1252 | 1100 | 0.0314 | - |
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+ | 0.1309 | 1150 | 0.0557 | - |
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+ | 0.1366 | 1200 | 0.0027 | - |
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+ | 0.1423 | 1250 | 0.0387 | - |
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+ | 0.1480 | 1300 | 0.0026 | - |
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+ | 0.1537 | 1350 | 0.044 | - |
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+ | 0.1594 | 1400 | 0.0499 | - |
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+ | 0.1651 | 1450 | 0.001 | - |
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+ | 0.1707 | 1500 | 0.0007 | - |
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+ | 0.1764 | 1550 | 0.0008 | - |
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+ | 0.1821 | 1600 | 0.0009 | - |
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+ | 0.1878 | 1650 | 0.053 | - |
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+ | 0.1935 | 1700 | 0.1111 | - |
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+ | 0.1992 | 1750 | 0.0018 | - |
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+ | 0.2049 | 1800 | 0.0009 | - |
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+ | 0.2106 | 1850 | 0.0008 | - |
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+ | 0.2163 | 1900 | 0.0011 | - |
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+ | 0.2220 | 1950 | 0.0042 | - |
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+ | 0.2277 | 2000 | 0.0005 | - |
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+ | 0.2334 | 2050 | 0.0023 | - |
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+ | 0.2390 | 2100 | 0.0003 | - |
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+ | 0.2504 | 2200 | 0.055 | - |
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+ | 0.2561 | 2250 | 0.0584 | - |
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+ | 0.2618 | 2300 | 0.06 | - |
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+ | 0.2675 | 2350 | 0.0004 | - |
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+ | 0.2732 | 2400 | 0.0022 | - |
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+ | 0.2846 | 2500 | 0.0014 | - |
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+ | 0.2903 | 2550 | 0.0008 | - |
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+ | 0.3017 | 2650 | 0.0118 | - |
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+ | 0.3073 | 2700 | 0.0892 | - |
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+ | 0.3130 | 2750 | 0.0004 | - |
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+ | 0.3187 | 2800 | 0.0061 | - |
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+ | 0.3244 | 2850 | 0.0601 | - |
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+ | 0.3301 | 2900 | 0.0003 | - |
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+ | 0.3415 | 3000 | 0.0006 | - |
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+
684
+ ### Framework Versions
685
+ - Python: 3.10.12
686
+ - SetFit: 1.0.3
687
+ - Sentence Transformers: 3.3.1
688
+ - Transformers: 4.41.2
689
+ - PyTorch: 2.1.0+cu118
690
+ - Datasets: 2.20.0
691
+ - Tokenizers: 0.19.1
692
+
693
+ ## Citation
694
+
695
+ ### BibTeX
696
+ ```bibtex
697
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
698
+ doi = {10.48550/ARXIV.2209.11055},
699
+ url = {https://arxiv.org/abs/2209.11055},
700
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
701
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
702
+ title = {Efficient Few-Shot Learning Without Prompts},
703
+ publisher = {arXiv},
704
+ year = {2022},
705
+ copyright = {Creative Commons Attribution 4.0 International}
706
+ }
707
+ ```
708
+
709
+ <!--
710
+ ## Glossary
711
+
712
+ *Clearly define terms in order to be accessible across audiences.*
713
+ -->
714
+
715
+ <!--
716
+ ## Model Card Authors
717
+
718
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
719
+ -->
720
+
721
+ <!--
722
+ ## Model Card Contact
723
+
724
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
725
+ -->
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@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cls_token": {
3
+ "content": "[CLS]",
4
+ "lstrip": false,
5
+ "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": {
10
+ "content": "[MASK]",
11
+ "lstrip": false,
12
+ "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]",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
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+ "single_word": false
22
+ },
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+ "sep_token": {
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+ "content": "[SEP]",
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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]",
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+ "lstrip": false,
33
+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ }
37
+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
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+ "0": {
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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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+ "100": {
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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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+ "101": {
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+ "content": "[CLS]",
21
+ "lstrip": false,
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+ "normalized": false,
23
+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "102": {
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+ "content": "[SEP]",
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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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+ "103": {
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+ "content": "[MASK]",
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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
42
+ }
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+ },
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+ "clean_up_tokenization_spaces": true,
45
+ "cls_token": "[CLS]",
46
+ "do_lower_case": false,
47
+ "mask_token": "[MASK]",
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+ "model_max_length": 512,
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+ "pad_token": "[PAD]",
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+ "sep_token": "[SEP]",
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+ "strip_accents": null,
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+ "tokenize_chinese_chars": true,
53
+ "tokenizer_class": "BertTokenizer",
54
+ "unk_token": "[UNK]"
55
+ }
vocab.txt ADDED
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