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spaly99
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my-setfit-model-dataset-PG-OCR

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
Model card Files Community
spaly99 commited on
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Add SetFit model

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Files changed (13) hide show
  1. 1_Pooling/config.json +9 -0
  2. README.md +849 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +7 -0
  5. config_setfit.json +4 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +51 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +66 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false
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+ }
README.md ADDED
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+ ---
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+ library_name: setfit
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ metrics:
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+ - accuracy
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+ - precision
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+ - recall
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+ - f1
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+ widget:
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+ - text: Google Maps
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+ - text: 'IN NEED OF OBEDIENCE CLASSES? '
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+ - text: ' .modal-content '
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+ - text: 'U Pere ris, AM sees FULLUW! \SfkE Ka £'' | '
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+ - text: 'exclusively MAX FACTOR Beeiting new lipstick concept makes all others obsolete! '
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+ pipeline_tag: text-classification
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+ inference: true
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+ base_model: sentence-transformers/paraphrase-mpnet-base-v2
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+ 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: 0.5003125
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+ name: Accuracy
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+ - type: precision
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+ value: 0.0
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+ name: Precision
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+ - type: recall
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+ value: 0.0
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+ name: Recall
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+ - type: f1
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+ value: 0.0
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+ name: F1
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+ ---
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+
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+ # SetFit with sentence-transformers/paraphrase-mpnet-base-v2
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 2 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | False | <ul><li>'Persistent'</li><li>'Forensic Contract'</li><li>'View Vendor List'</li></ul> |
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+ | True | <ul><li>'winming camp at Taj Deccan. Morning and evening batches. Start today. Become a champ. Monday to Friday till 3 Ist march '</li><li>'您的反馈已记录，我们将努力改善您的浏览体验。'</li><li>'Ve ConcerrualL DESIGNER '</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 | Precision | Recall | F1 |
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+ |:--------|:---------|:----------|:-------|:----|
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+ | **all** | 0.5003 | 0.0 | 0.0 | 0.0 |
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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("Google Maps")
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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 | 8.5055 | 706 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | False | 6399 |
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+ | True | 6401 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 2)
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+ - num_epochs: (1, 16)
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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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+ - run_name: PG-OCR-test-1
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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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+ | 0.0000 | 1 | 0.5 | - |
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+ | 0.0016 | 50 | 0.5 | - |
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+ | 0.0031 | 100 | 0.5 | - |
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+ | 0.0047 | 150 | 0.5 | - |
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+ | 0.0063 | 200 | 0.5 | - |
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+ | 0.0078 | 250 | 0.5 | - |
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+ | 0.0094 | 300 | 0.5 | - |
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+ | 0.0109 | 350 | 0.5 | - |
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+ | 0.0125 | 400 | 0.5 | - |
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+ | 0.0141 | 450 | 0.5 | - |
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+ | 0.0156 | 500 | 0.5 | - |
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+ | 0.0172 | 550 | 0.5 | - |
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+ | 0.0187 | 600 | 0.5 | - |
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+ | 0.0203 | 650 | 0.5 | - |
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+ | 0.0219 | 700 | 0.5 | - |
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+ | 0.0234 | 750 | 0.5 | - |
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+ | 0.025 | 800 | 0.5 | - |
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+ | 0.0266 | 850 | 0.5 | - |
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+ | 0.0281 | 900 | 0.5 | - |
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+ | 0.0297 | 950 | 0.5 | - |
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+ | 0.0312 | 1000 | 0.5 | - |
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+ | 0.0328 | 1050 | 0.5 | - |
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+ | 0.0344 | 1100 | 0.5 | - |
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+ | 0.0359 | 1150 | 0.5 | - |
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+ | 0.0375 | 1200 | 0.5 | - |
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+ | 0.0391 | 1250 | 0.5 | - |
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+ | 0.0406 | 1300 | 0.5 | - |
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+ | 0.0422 | 1350 | 0.5 | - |
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+ | 0.0437 | 1400 | 0.5 | - |
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+ | 0.0469 | 1500 | 0.5 | - |
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380
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381
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382
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383
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384
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385
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386
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387
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388
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389
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390
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391
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392
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393
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394
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395
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396
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397
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398
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399
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400
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401
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402
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403
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404
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405
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406
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407
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408
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409
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410
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411
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412
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413
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414
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415
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416
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417
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418
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419
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420
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421
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422
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423
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424
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425
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426
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427
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428
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429
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430
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431
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432
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433
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434
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435
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436
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437
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438
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439
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440
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441
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442
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443
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444
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445
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446
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447
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448
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449
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450
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451
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452
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453
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454
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455
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456
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457
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458
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459
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460
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461
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462
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463
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464
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465
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466
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467
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468
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469
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470
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471
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472
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473
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474
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475
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476
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477
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478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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493
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494
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495
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496
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497
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498
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499
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500
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501
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502
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503
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504
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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552
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553
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554
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555
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556
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557
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558
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559
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560
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561
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562
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563
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564
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565
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566
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567
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568
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569
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570
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571
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572
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573
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574
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575
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576
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577
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578
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579
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580
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581
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582
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583
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584
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585
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586
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587
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588
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589
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590
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591
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592
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593
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594
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595
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596
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597
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598
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599
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600
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601
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602
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603
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604
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605
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606
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607
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608
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609
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610
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611
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612
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613
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614
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615
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616
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617
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618
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619
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620
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621
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622
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623
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624
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625
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626
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627
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628
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629
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630
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631
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632
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633
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634
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635
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636
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637
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638
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639
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640
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641
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642
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643
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644
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645
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646
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647
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648
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649
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650
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651
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652
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653
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654
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655
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656
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657
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658
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659
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660
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661
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662
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663
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664
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665
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666
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667
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668
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669
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670
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671
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672
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673
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674
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675
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676
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677
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678
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679
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680
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681
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682
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683
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684
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685
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686
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687
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688
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689
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690
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691
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692
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693
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694
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695
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696
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697
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698
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699
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700
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701
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702
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703
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704
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705
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706
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707
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708
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709
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710
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711
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712
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713
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714
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715
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716
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717
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718
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719
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720
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721
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722
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723
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724
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725
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726
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727
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728
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729
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730
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731
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732
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733
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734
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735
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736
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737
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738
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739
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740
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741
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742
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743
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744
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745
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746
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747
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748
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749
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750
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751
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752
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753
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754
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755
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756
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757
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758
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759
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760
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761
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762
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763
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764
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765
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766
+ | 0.9375 | 30000 | 0.5 | 0.5 |
767
+ | 0.9391 | 30050 | 0.5 | - |
768
+ | 0.9406 | 30100 | 0.5 | - |
769
+ | 0.9422 | 30150 | 0.5 | - |
770
+ | 0.9437 | 30200 | 0.5 | - |
771
+ | 0.9453 | 30250 | 0.5 | - |
772
+ | 0.9469 | 30300 | 0.5 | - |
773
+ | 0.9484 | 30350 | 0.5 | - |
774
+ | 0.95 | 30400 | 0.5 | - |
775
+ | 0.9516 | 30450 | 0.5 | - |
776
+ | 0.9531 | 30500 | 0.5 | - |
777
+ | 0.9547 | 30550 | 0.5 | - |
778
+ | 0.9563 | 30600 | 0.5 | - |
779
+ | 0.9578 | 30650 | 0.5 | - |
780
+ | 0.9594 | 30700 | 0.5 | - |
781
+ | 0.9609 | 30750 | 0.5 | - |
782
+ | 0.9625 | 30800 | 0.5 | - |
783
+ | 0.9641 | 30850 | 0.5 | - |
784
+ | 0.9656 | 30900 | 0.5 | - |
785
+ | 0.9672 | 30950 | 0.5 | - |
786
+ | 0.9688 | 31000 | 0.5 | - |
787
+ | 0.9703 | 31050 | 0.5 | - |
788
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789
+ | 0.9734 | 31150 | 0.5 | - |
790
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791
+ | 0.9766 | 31250 | 0.5 | - |
792
+ | 0.9781 | 31300 | 0.5 | - |
793
+ | 0.9797 | 31350 | 0.5 | - |
794
+ | 0.9812 | 31400 | 0.5 | - |
795
+ | 0.9828 | 31450 | 0.5 | - |
796
+ | 0.9844 | 31500 | 0.5 | - |
797
+ | 0.9859 | 31550 | 0.5 | - |
798
+ | 0.9875 | 31600 | 0.5 | - |
799
+ | 0.9891 | 31650 | 0.5 | - |
800
+ | 0.9906 | 31700 | 0.5 | - |
801
+ | 0.9922 | 31750 | 0.5 | - |
802
+ | 0.9938 | 31800 | 0.5 | - |
803
+ | 0.9953 | 31850 | 0.5 | - |
804
+ | 0.9969 | 31900 | 0.5 | - |
805
+ | 0.9984 | 31950 | 0.5 | - |
806
+ | 1.0 | 32000 | 0.5 | - |
807
+
808
+ ### Framework Versions
809
+ - Python: 3.11.0
810
+ - SetFit: 1.0.3
811
+ - Sentence Transformers: 2.3.0
812
+ - Transformers: 4.37.2
813
+ - PyTorch: 2.2.1+cu121
814
+ - Datasets: 2.16.1
815
+ - Tokenizers: 0.15.1
816
+
817
+ ## Citation
818
+
819
+ ### BibTeX
820
+ ```bibtex
821
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
822
+ doi = {10.48550/ARXIV.2209.11055},
823
+ url = {https://arxiv.org/abs/2209.11055},
824
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
825
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
826
+ title = {Efficient Few-Shot Learning Without Prompts},
827
+ publisher = {arXiv},
828
+ year = {2022},
829
+ copyright = {Creative Commons Attribution 4.0 International}
830
+ }
831
+ ```
832
+
833
+ <!--
834
+ ## Glossary
835
+
836
+ *Clearly define terms in order to be accessible across audiences.*
837
+ -->
838
+
839
+ <!--
840
+ ## Model Card Authors
841
+
842
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
843
+ -->
844
+
845
+ <!--
846
+ ## Model Card Contact
847
+
848
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
849
+ -->
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