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egis-group
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router_mini_lm_l12

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files
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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 +471 -0
  3. config.json +26 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +7 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +20 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +37 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +64 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 384,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md 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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+ base_model: sentence-transformers/all-MiniLM-L12-v2
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+ metrics:
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+ - accuracy
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+ widget:
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+ - text: Quel est le principal litige dans les projets de construction, et quel droit
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+ de la partie accusee
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+ - text: Clarifier quels sont les facteurs déterminants dans le choix d'un emplacement
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+ pour un nouveau magasin
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+ - text: Compare ces deux documents
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+ - text: Can you explain the process of wind energy generation and discuss its environmental
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+ impacts compared to those of hydroelectric power?
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+ - text: Could you restate the advantages of using project management software that
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+ were mentioned earlier? Provide a linkedin post about it
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+ pipeline_tag: text-classification
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+ inference: true
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+ model-index:
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+ - name: SetFit with sentence-transformers/all-MiniLM-L12-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: 0.9333333333333333
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with sentence-transformers/all-MiniLM-L12-v2
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-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:** 128 tokens
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+ - **Number of Classes:** 5 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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+ | sub_queries | <ul><li>'Could you break down the main factors I should consider when researching market prices and how to effectively communicate our needs to the supplier during negotiations?'</li><li>'Comment faire pousser une plante et le mesurer ?'</li><li>"Quel est le meilleur matériau pour l'isolation phonique et thermique?"</li></ul> |
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+ | simple_questions | <ul><li>'What are the key strategies for maintaining efficient communication in a remote work environment?'</li><li>'Could you summarize the ways a person can help in adapting to climate change ?'</li><li>'What are the current trends in construction?'</li></ul> |
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+ | exchange | <ul><li>'Could you please restate your last explanation using simpler terms?'</li><li>'Could you restate the impact of augmented reality on design practices?'</li><li>'Pourriez-vous me donner un résumé des principaux points abordés dans notre conversation précédente ?'</li></ul> |
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+ | compare | <ul><li>'How do the conclusions differ?'</li><li>'Contrast the main arguments presented in each paper'</li><li>'Quelles sont les principales différences dans les programmes éducatifs décrits dans ces documents ?'</li></ul> |
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+ | summary | <ul><li>'Que dois-je retenir de ce doc ?'</li><li>'What are the key assertions made within the text'</li><li>'What are the most important argument stated in the document?'</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** | 0.9333 |
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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("egis-group/router_mini_lm_l12")
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+ # Run inference
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+ preds = model("Compare ces deux documents")
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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 | 13.4389 | 48 |
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+
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+ | Label | Training Sample Count |
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+ |:---------|:----------------------|
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+ | negative | 0 |
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+ | positive | 0 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 16)
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+ - num_epochs: (4, 4)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - body_learning_rate: (2e-05, 1e-05)
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+ - head_learning_rate: 0.01
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: False
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+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: True
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:-------:|:---------:|:-------------:|:---------------:|
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+ | 0.0003 | 1 | 0.4073 | - |
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+ | 0.0151 | 50 | 0.3054 | - |
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+ | 0.0303 | 100 | 0.2066 | - |
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+ | 0.0454 | 150 | 0.2664 | - |
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+ | 0.0606 | 200 | 0.2463 | - |
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+ | 0.0757 | 250 | 0.214 | - |
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+ | 0.0909 | 300 | 0.1892 | - |
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+ | 0.1060 | 350 | 0.1402 | - |
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+ | 0.1212 | 400 | 0.1804 | - |
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+ | 0.1363 | 450 | 0.0571 | - |
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+ | 0.1515 | 500 | 0.0979 | - |
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+ | 0.1666 | 550 | 0.1775 | - |
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+ | 0.1818 | 600 | 0.0377 | - |
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+ | 0.1969 | 650 | 0.0398 | - |
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+ | 0.2121 | 700 | 0.0423 | - |
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+ | 0.2272 | 750 | 0.0036 | - |
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+ | 0.2424 | 800 | 0.0079 | - |
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+ | 0.2575 | 850 | 0.0049 | - |
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+ | 0.2726 | 900 | 0.0018 | - |
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+ | 0.2878 | 950 | 0.0018 | - |
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+ | 0.3029 | 1000 | 0.0032 | - |
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+ | 0.3181 | 1050 | 0.0019 | - |
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+ | 0.3332 | 1100 | 0.0008 | - |
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+ | 0.3484 | 1150 | 0.0006 | - |
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+ | 0.3635 | 1200 | 0.0006 | - |
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+ | 0.3787 | 1250 | 0.0011 | - |
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+ | 0.3938 | 1300 | 0.0005 | - |
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+ | 0.4090 | 1350 | 0.001 | - |
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+ | 0.4241 | 1400 | 0.0009 | - |
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+ | 0.4393 | 1450 | 0.0004 | - |
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+ | 0.4544 | 1500 | 0.0003 | - |
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+ | 0.4696 | 1550 | 0.0003 | - |
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+ | 0.4847 | 1600 | 0.0006 | - |
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+ | 0.4998 | 1650 | 0.0003 | - |
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+ | 0.5150 | 1700 | 0.0002 | - |
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+ | 0.5301 | 1750 | 0.0002 | - |
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+ | 0.5453 | 1800 | 0.0005 | - |
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+ | 0.5604 | 1850 | 0.0003 | - |
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+ | 0.5756 | 1900 | 0.0002 | - |
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+ | 0.5907 | 1950 | 0.0002 | - |
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+ | 0.6059 | 2000 | 0.0001 | - |
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+ | 0.6210 | 2050 | 0.0002 | - |
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+ | 0.6362 | 2100 | 0.0002 | - |
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+ | 0.6513 | 2150 | 0.0001 | - |
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+ | 0.6665 | 2200 | 0.0002 | - |
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+ | 0.6816 | 2250 | 0.0002 | - |
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+ | 0.6968 | 2300 | 0.0002 | - |
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+ | 0.7119 | 2350 | 0.0002 | - |
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+ | 0.7271 | 2400 | 0.0002 | - |
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+ | 0.7422 | 2450 | 0.0002 | - |
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+ | 0.7573 | 2500 | 0.0001 | - |
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+ | 0.7725 | 2550 | 0.0001 | - |
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+ | 0.7876 | 2600 | 0.0002 | - |
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+ | 0.8028 | 2650 | 0.0001 | - |
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+ | 0.8179 | 2700 | 0.0002 | - |
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+ | 0.8331 | 2750 | 0.0007 | - |
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+ | 0.8482 | 2800 | 0.0001 | - |
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+ | 0.8634 | 2850 | 0.0001 | - |
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+ | 0.8785 | 2900 | 0.0001 | - |
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+ | 0.8937 | 2950 | 0.0001 | - |
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+ | 0.9088 | 3000 | 0.0001 | - |
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+ | 0.9240 | 3050 | 0.0002 | - |
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+ | 0.9391 | 3100 | 0.0001 | - |
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+ | 0.9543 | 3150 | 0.0001 | - |
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+ | 0.9694 | 3200 | 0.0001 | - |
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+ | 0.9846 | 3250 | 0.0001 | - |
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+ | 0.9997 | 3300 | 0.0002 | - |
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+ | 1.0 | 3301 | - | 0.0001 |
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+ | 1.0148 | 3350 | 0.0003 | - |
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+ | 1.0300 | 3400 | 0.0002 | - |
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+ | 1.0451 | 3450 | 0.0001 | - |
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+ | 1.9994 | 6600 | 0.0 | - |
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+ | 2.0 | 6602 | - | 0.0 |
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+ | 2.0145 | 6650 | 0.0 | - |
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+ | 2.0297 | 6700 | 0.0 | - |
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+ | **4.0** | **13204** | **-** | **0.0** |
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+
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+ * The bold row denotes the saved checkpoint.
430
+ ### Framework Versions
431
+ - Python: 3.10.12
432
+ - SetFit: 1.0.3
433
+ - Sentence Transformers: 3.0.1
434
+ - Transformers: 4.39.0
435
+ - PyTorch: 2.3.0+cu121
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+ - Datasets: 2.19.2
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+ - Tokenizers: 0.15.2
438
+
439
+ ## Citation
440
+
441
+ ### BibTeX
442
+ ```bibtex
443
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
444
+ doi = {10.48550/ARXIV.2209.11055},
445
+ url = {https://arxiv.org/abs/2209.11055},
446
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
447
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
448
+ title = {Efficient Few-Shot Learning Without Prompts},
449
+ publisher = {arXiv},
450
+ year = {2022},
451
+ copyright = {Creative Commons Attribution 4.0 International}
452
+ }
453
+ ```
454
+
455
+ <!--
456
+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
459
+ -->
460
+
461
+ <!--
462
+ ## 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.*
465
+ -->
466
+
467
+ <!--
468
+ ## 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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+ -->
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