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victomoe
/
setfit-intent-classifier-2

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Model card Files Community
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Commit
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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +305 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +12 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +51 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +59 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_weightedmean_tokens": 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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+ metrics:
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+ - accuracy
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+ widget:
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+ - text: I'd like to go up one floor
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+ - text: I’d like to go to floor 2.
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+ - text: Which office is Yngvar located in?
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+ - text: Yes, proceed.
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+ - text: Absolutely.
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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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+ ---
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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:** 7 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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+ | RequestMoveToFloor | <ul><li>'Please go to the 3rd floor.'</li><li>'Can you take me to floor 5?'</li><li>'I need to go to the 8th floor.'</li></ul> |
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+ | RequestMoveToFloorByX | <ul><li>'Go one floor up'</li><li>'Take me up two floors'</li><li>'Move me down one level'</li></ul> |
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+ | Confirm | <ul><li>"Yes, that's right."</li><li>'Sure.'</li><li>'Exactly.'</li></ul> |
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+ | RequestEmployeeLocation | <ul><li>'Where is Erik Velldal’s office?'</li><li>'Which floor is Andreas Austeng on?'</li><li>'Can you tell me where Birthe Soppe’s office is?'</li></ul> |
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+ | CurrentFloor | <ul><li>'Which floor are we on?'</li><li>'What floor is this?'</li><li>'Are we on the 5th floor?'</li></ul> |
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+ | Stop | <ul><li>'Stop the elevator.'</li><li>"Wait, don't go to that floor."</li><li>'No, not that floor.'</li></ul> |
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+ | OutOfCoverage | <ul><li>"What's the capital of France?"</li><li>'How many floors does this building have?'</li><li>'Can you make a phone call for me?'</li></ul> |
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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("victomoe/setfit-intent-classifier-2")
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+ # Run inference
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+ preds = model("Absolutely.")
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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 | 5.1533 | 9 |
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+
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+ | Label | Training Sample Count |
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+ |:------------------------|:----------------------|
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+ | Confirm | 22 |
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+ | CurrentFloor | 21 |
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+ | OutOfCoverage | 22 |
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+ | RequestEmployeeLocation | 22 |
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+ | RequestMoveToFloor | 23 |
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+ | RequestMoveToFloorByX | 20 |
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+ | Stop | 20 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (32, 32)
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+ - num_epochs: (10, 10)
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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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+ - l2_weight: 0.01
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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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+ | 0.0017 | 1 | 0.1415 | - |
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+ | 0.0829 | 50 | 0.1863 | - |
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+ | 0.1658 | 100 | 0.1559 | - |
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+ | 0.2488 | 150 | 0.0966 | - |
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+ | 0.3317 | 200 | 0.0363 | - |
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+ | 0.4146 | 250 | 0.009 | - |
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+ | 0.4975 | 300 | 0.0035 | - |
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+ | 0.5804 | 350 | 0.0024 | - |
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+ | 0.6633 | 400 | 0.0017 | - |
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+ | 0.7463 | 450 | 0.0015 | - |
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+ | 0.8292 | 500 | 0.0011 | - |
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+ | 0.9121 | 550 | 0.0009 | - |
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+ | 0.9950 | 600 | 0.0008 | - |
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+ | 1.0779 | 650 | 0.0007 | - |
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+ | 1.1609 | 700 | 0.0006 | - |
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+ | 1.2438 | 750 | 0.0005 | - |
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+ | 1.3267 | 800 | 0.0005 | - |
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+
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+ ### Framework Versions
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+ - Python: 3.10.8
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+ - SetFit: 1.1.0
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+ - Sentence Transformers: 3.1.1
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+ - Transformers: 4.38.2
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+ - PyTorch: 2.1.2
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+ - Datasets: 2.17.1
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+ - Tokenizers: 0.15.0
272
+
273
+ ## Citation
274
+
275
+ ### BibTeX
276
+ ```bibtex
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+ @article{https://doi.org/10.48550/arxiv.2209.11055,
278
+ doi = {10.48550/ARXIV.2209.11055},
279
+ url = {https://arxiv.org/abs/2209.11055},
280
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
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+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
282
+ title = {Efficient Few-Shot Learning Without Prompts},
283
+ publisher = {arXiv},
284
+ year = {2022},
285
+ copyright = {Creative Commons Attribution 4.0 International}
286
+ }
287
+ ```
288
+
289
+ <!--
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+ ## Glossary
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+
292
+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
298
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
299
+ -->
300
+
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+ <!--
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+ ## Model Card Contact
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+
304
+ *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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tokenizer_config.json ADDED
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1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "<s>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "1": {
12
+ "content": "<pad>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "2": {
20
+ "content": "</s>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "104": {
28
+ "content": "[UNK]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "30526": {
36
+ "content": "<mask>",
37
+ "lstrip": true,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "bos_token": "<s>",
45
+ "clean_up_tokenization_spaces": true,
46
+ "cls_token": "<s>",
47
+ "do_basic_tokenize": true,
48
+ "do_lower_case": true,
49
+ "eos_token": "</s>",
50
+ "mask_token": "<mask>",
51
+ "model_max_length": 512,
52
+ "never_split": null,
53
+ "pad_token": "<pad>",
54
+ "sep_token": "</s>",
55
+ "strip_accents": null,
56
+ "tokenize_chinese_chars": true,
57
+ "tokenizer_class": "MPNetTokenizer",
58
+ "unk_token": "[UNK]"
59
+ }
vocab.txt ADDED
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