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victomoe
/
setfit-intent-classifier

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Model card Files Community
victomoe commited on
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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +348 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +13 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +51 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +59 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ 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: What's today's date?
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+ - text: Yes, please.
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+ - text: I’d like to go to floor 2.
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+ - text: Alright, floor 1 it is.
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+ - text: Which floor can I find Martin Giese on?
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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:** 8 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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+ | 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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+ | Feedback | <ul><li>'Okay, going to the 3rd floor.'</li><li>'Sure, heading to floor 5.'</li><li>'Understood, taking you to the 8th floor.'</li></ul> |
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+ | Repeat | <ul><li>'Can you repeat that?'</li><li>'Sorry, I didn’t get that. Can you say it again?'</li><li>'What was that?'</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")
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+ # Run inference
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+ preds = model("Yes, please.")
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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 | 5.2267 | 10 |
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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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+ | Feedback | 22 |
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+ | OutOfCoverage | 22 |
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+ | Repeat | 20 |
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+ | RequestEmployeeLocation | 22 |
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+ | RequestMoveToFloor | 23 |
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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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+ | 0.0012 | 1 | 0.1997 | - |
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+ | 0.0618 | 50 | 0.1876 | - |
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+ | 0.1236 | 100 | 0.1623 | - |
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+ | 0.1854 | 150 | 0.1266 | - |
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+ | 0.2472 | 200 | 0.0748 | - |
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+ | 0.3090 | 250 | 0.0417 | - |
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+ | 0.3708 | 300 | 0.0236 | - |
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+ | 0.4326 | 350 | 0.0094 | - |
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+ | 0.4944 | 400 | 0.0041 | - |
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+ | 0.5562 | 450 | 0.0028 | - |
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+ | 0.6180 | 500 | 0.002 | - |
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+ | 0.6799 | 550 | 0.0016 | - |
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+ | 0.7417 | 600 | 0.0013 | - |
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+ | 0.8035 | 650 | 0.0011 | - |
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+ | 0.8653 | 700 | 0.0009 | - |
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+ | 0.9271 | 750 | 0.0008 | - |
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+ | 0.9889 | 800 | 0.0007 | - |
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+ | 1.0507 | 850 | 0.0007 | - |
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+ | 1.1125 | 900 | 0.0006 | - |
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+ | 1.1743 | 950 | 0.0006 | - |
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+ | 1.2361 | 1000 | 0.0005 | - |
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+ | 1.2979 | 1050 | 0.0005 | - |
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+ | 1.3597 | 1100 | 0.0004 | - |
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+ | 1.4215 | 1150 | 0.0004 | - |
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+ | 1.4833 | 1200 | 0.0004 | - |
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+ | 1.5451 | 1250 | 0.0004 | - |
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+ | 1.6069 | 1300 | 0.0004 | - |
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+ | 1.6687 | 1350 | 0.0003 | - |
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+ | 1.7305 | 1400 | 0.0003 | - |
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+ | 1.7923 | 1450 | 0.0003 | - |
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+ | 1.8541 | 1500 | 0.0003 | - |
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+ | 1.9159 | 1550 | 0.0003 | - |
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+ | 1.9778 | 1600 | 0.0003 | - |
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+ | 2.0396 | 1650 | 0.0003 | - |
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+ | 2.1014 | 1700 | 0.0003 | - |
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+ | 2.1632 | 1750 | 0.0003 | - |
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+ | 2.2250 | 1800 | 0.0002 | - |
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+ | 2.2868 | 1850 | 0.0002 | - |
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+ | 2.3486 | 1900 | 0.0002 | - |
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+ | 2.4104 | 1950 | 0.0002 | - |
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+ | 2.4722 | 2000 | 0.0002 | - |
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+ | 2.8430 | 2300 | 0.0002 | - |
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+ | 2.9048 | 2350 | 0.0002 | - |
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+ | 2.9666 | 2400 | 0.0002 | - |
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+ | 3.0284 | 2450 | 0.0002 | - |
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+ | 3.7083 | 3000 | 0.0002 | - |
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+ | 3.7701 | 3050 | 0.0001 | - |
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+ | 3.8319 | 3100 | 0.0001 | - |
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+ | 3.8937 | 3150 | 0.0001 | - |
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+ | 3.9555 | 3200 | 0.0001 | - |
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+ | 4.0791 | 3300 | 0.0001 | - |
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+ | 8.8999 | 7200 | 0.0001 | - |
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+ | 9.0235 | 7300 | 0.0001 | - |
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+ | 9.0853 | 7350 | 0.0001 | - |
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+ | 9.1471 | 7400 | 0.0001 | - |
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+ | 9.2089 | 7450 | 0.0001 | - |
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+ | 9.2707 | 7500 | 0.0001 | - |
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+ | 9.3325 | 7550 | 0.0001 | - |
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+ | 9.3943 | 7600 | 0.0001 | - |
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+ | 9.4561 | 7650 | 0.0001 | - |
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+ | 9.5179 | 7700 | 0.0001 | - |
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+ | 9.5797 | 7750 | 0.0001 | - |
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+ | 9.6415 | 7800 | 0.0001 | - |
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+ | 9.7033 | 7850 | 0.0001 | - |
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+ | 9.7651 | 7900 | 0.0001 | - |
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+ | 9.8269 | 7950 | 0.0001 | - |
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+ | 9.8888 | 8000 | 0.0001 | - |
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+ | 9.9506 | 8050 | 0.0001 | - |
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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
312
+ - PyTorch: 2.1.2
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+ - Datasets: 2.17.1
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+ - Tokenizers: 0.15.0
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+
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+ ## Citation
317
+
318
+ ### BibTeX
319
+ ```bibtex
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+ @article{https://doi.org/10.48550/arxiv.2209.11055,
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+ doi = {10.48550/ARXIV.2209.11055},
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+ url = {https://arxiv.org/abs/2209.11055},
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+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
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+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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+ title = {Efficient Few-Shot Learning Without Prompts},
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+ publisher = {arXiv},
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+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
329
+ }
330
+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
config.json ADDED
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+ {
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+ "_name_or_path": "sentence-transformers/paraphrase-mpnet-base-v2",
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+ "architectures": [
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+ "MPNetModel"
5
+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "bos_token_id": 0,
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+ "eos_token_id": 2,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "layer_norm_eps": 1e-05,
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+ "max_position_embeddings": 514,
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+ "model_type": "mpnet",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 1,
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+ "relative_attention_num_buckets": 32,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.38.2",
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+ "vocab_size": 30527
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+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "__version__": {
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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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+ },
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+ "prompts": {},
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+ "default_prompt_name": null,
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+ "similarity_fn_name": null
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+ }
config_setfit.json ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "normalize_embeddings": false,
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+ "labels": [
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+ "Confirm",
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+ "CurrentFloor",
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+ "Feedback",
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+ "OutOfCoverage",
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+ "Repeat",
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+ "RequestEmployeeLocation",
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+ "RequestMoveToFloor",
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+ "Stop"
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+ ]
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+ }
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