Commit
•
0bb8dba
1
Parent(s):
5f65756
Add SetFit model
Browse files- 1_Pooling/config.json +7 -0
- README.md +217 -0
- config.json +26 -0
- config_sentence_transformers.json +7 -0
- config_setfit.json +4 -0
- model.safetensors +3 -0
- model_head.pkl +3 -0
- modules.json +20 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +62 -0
- vocab.txt +0 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 1024,
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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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}
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README.md
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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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datasets:
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- dvilasuero/banking77-topics-setfit
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metrics:
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- accuracy
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widget:
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- text: I requested a refund, and never received it. What can I do?
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- text: I have a 1 euro fee on my statement.
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- text: I would like an account for my children, how do I go about doing this?
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- text: What do I need to do to transfer money into my account?
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- text: Which country's currency do you support?
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pipeline_tag: text-classification
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inference: true
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base_model: thenlper/gte-large
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model-index:
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- name: SetFit with thenlper/gte-large
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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: dvilasuero/banking77-topics-setfit
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type: dvilasuero/banking77-topics-setfit
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split: test
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metrics:
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- type: accuracy
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value: 0.9230769230769231
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name: Accuracy
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---
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# SetFit with thenlper/gte-large
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This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [dvilasuero/banking77-topics-setfit](https://huggingface.co/datasets/dvilasuero/banking77-topics-setfit) dataset that can be used for Text Classification. This SetFit model uses [thenlper/gte-large](https://huggingface.co/thenlper/gte-large) 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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The model has been trained using an efficient few-shot learning technique that involves:
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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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## Model Details
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### Model Description
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- **Model Type:** SetFit
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- **Sentence Transformer body:** [thenlper/gte-large](https://huggingface.co/thenlper/gte-large)
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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:** [dvilasuero/banking77-topics-setfit](https://huggingface.co/datasets/dvilasuero/banking77-topics-setfit)
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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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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### Model Labels
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| Label | Examples |
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|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| 2 | <ul><li>'The money I transferred does not show in the balance.'</li><li>'I was wondering how I could have two charges for the same item happen more than once in a 7 day period. Is there anyway I could get this corrected asap.'</li><li>'What is the source of my available funds?'</li></ul> |
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| 0 | <ul><li>'Do you support the EU?'</li><li>"Can you freeze my account? I just saw there are transactions on my account that I don't recognize. How can I fix this?"</li><li>'Please close my account. I am unsatisfied with your service.'</li></ul> |
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| 5 | <ul><li>'Are you able to unblock my pin?'</li><li>'I can not find my card pin.'</li><li>'If I need a PIN for my card, where is it located?'</li></ul> |
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| 1 | <ul><li>"I can't get money out of the ATM"</li><li>'Where can I use this card at an ATM?'</li><li>'Can I use my card at any ATMs?'</li></ul> |
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| 3 | <ul><li>'Can I get cash with this card anywhere?'</li><li>'Can you please show me where I can find the location to link my card?'</li><li>'Am I able to get a card in EU?'</li></ul> |
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| 6 | <ul><li>'My friends want to top up my account'</li><li>'Can I be topped up once I hit a certain balance?'</li><li>'Can you tell me why my top up was reverted?'</li></ul> |
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| 7 | <ul><li>'How do I send my account money through transfer?'</li><li>'How do I transfer money to my account?'</li><li>'How can I transfer money from an outside bank?'</li></ul> |
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| 4 | <ul><li>'Do you work with all fiat currencies?'</li><li>'Can I exchange to EUR?'</li><li>'Is my country supported'</li></ul> |
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## Evaluation
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### Metrics
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| Label | Accuracy |
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|:--------|:---------|
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| **all** | 0.9231 |
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## Uses
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### Direct Use for Inference
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First install the SetFit library:
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```bash
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pip install setfit
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```
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Then you can load this model and run inference.
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```python
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from setfit import SetFitModel
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("HarshalBhg/gte-large-setfit-train-test2")
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# Run inference
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preds = model("I have a 1 euro fee on my statement.")
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```
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<!--
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### Downstream Use
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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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### Out-of-Scope Use
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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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## Bias, Risks and Limitations
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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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### Recommendations
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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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## Training Details
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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 | 10.5833 | 40 |
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| Label | Training Sample Count |
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|:------|:----------------------|
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| 0 | 10 |
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| 1 | 19 |
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| 2 | 28 |
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| 3 | 36 |
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| 4 | 13 |
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| 5 | 14 |
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| 6 | 15 |
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| 7 | 21 |
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### Training Hyperparameters
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- batch_size: (16, 16)
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- num_epochs: (1, 1)
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- max_steps: -1
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- sampling_strategy: oversampling
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- num_iterations: 20
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- body_learning_rate: (2e-05, 1e-05)
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- head_learning_rate: 0.01
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- loss: CosineSimilarityLoss
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- distance_metric: cosine_distance
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- margin: 0.25
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- end_to_end: False
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- use_amp: False
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- warmup_proportion: 0.1
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- seed: 42
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- eval_max_steps: -1
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- load_best_model_at_end: False
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:------:|:----:|:-------------:|:---------------:|
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| 0.0026 | 1 | 0.3183 | - |
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| 0.1282 | 50 | 0.0614 | - |
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| 0.2564 | 100 | 0.0044 | - |
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| 0.3846 | 150 | 0.001 | - |
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| 0.5128 | 200 | 0.0008 | - |
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| 0.6410 | 250 | 0.001 | - |
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| 0.7692 | 300 | 0.0006 | - |
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| 0.8974 | 350 | 0.0012 | - |
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### Framework Versions
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- Python: 3.10.12
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- SetFit: 1.0.1
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- Sentence Transformers: 2.2.2
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- Transformers: 4.35.2
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- PyTorch: 2.1.0+cu121
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- Datasets: 2.15.0
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- Tokenizers: 0.15.0
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## Citation
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### BibTeX
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```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}
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}
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```
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<!--
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## Glossary
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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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## Model Card Authors
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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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## Model Card Contact
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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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config.json
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{
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"_name_or_path": "/root/.cache/torch/sentence_transformers/thenlper_gte-large/",
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.35.2",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "2.2.2",
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"transformers": "4.35.2",
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"pytorch": "2.1.0+cu121"
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}
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}
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config_setfit.json
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{
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"labels": null,
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"normalize_embeddings": false
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:fc3c32568d9df4c4e88426d14efcfd9071cfb90e9f592466bda41f85b7bf5da6
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size 1340612432
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model_head.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:6ca4fb5e35421aeaf8fbc060fe40511d66674e53e544b703459636bf370afc28
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size 66487
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modules.json
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[
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{
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.models.Transformer"
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},
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{
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"idx": 1,
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"name": "1",
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11 |
+
"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
},
|
14 |
+
{
|
15 |
+
"idx": 2,
|
16 |
+
"name": "2",
|
17 |
+
"path": "2_Normalize",
|
18 |
+
"type": "sentence_transformers.models.Normalize"
|
19 |
+
}
|
20 |
+
]
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
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|
1 |
+
{
|
2 |
+
"max_seq_length": 512,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
special_tokens_map.json
ADDED
@@ -0,0 +1,7 @@
|
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|
|
|
|
1 |
+
{
|
2 |
+
"cls_token": "[CLS]",
|
3 |
+
"mask_token": "[MASK]",
|
4 |
+
"pad_token": "[PAD]",
|
5 |
+
"sep_token": "[SEP]",
|
6 |
+
"unk_token": "[UNK]"
|
7 |
+
}
|
tokenizer.json
ADDED
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|
tokenizer_config.json
ADDED
@@ -0,0 +1,62 @@
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|
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|
|
|
|
|
1 |
+
{
|
2 |
+
"added_tokens_decoder": {
|
3 |
+
"0": {
|
4 |
+
"content": "[PAD]",
|
5 |
+
"lstrip": false,
|
6 |
+
"normalized": false,
|
7 |
+
"rstrip": false,
|
8 |
+
"single_word": false,
|
9 |
+
"special": true
|
10 |
+
},
|
11 |
+
"100": {
|
12 |
+
"content": "[UNK]",
|
13 |
+
"lstrip": false,
|
14 |
+
"normalized": false,
|
15 |
+
"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
+
"101": {
|
20 |
+
"content": "[CLS]",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
},
|
27 |
+
"102": {
|
28 |
+
"content": "[SEP]",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"103": {
|
36 |
+
"content": "[MASK]",
|
37 |
+
"lstrip": false,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": true
|
42 |
+
}
|
43 |
+
},
|
44 |
+
"clean_up_tokenization_spaces": true,
|
45 |
+
"cls_token": "[CLS]",
|
46 |
+
"do_lower_case": true,
|
47 |
+
"mask_token": "[MASK]",
|
48 |
+
"max_length": 128,
|
49 |
+
"model_max_length": 1000000000000000019884624838656,
|
50 |
+
"pad_to_multiple_of": null,
|
51 |
+
"pad_token": "[PAD]",
|
52 |
+
"pad_token_type_id": 0,
|
53 |
+
"padding_side": "right",
|
54 |
+
"sep_token": "[SEP]",
|
55 |
+
"stride": 0,
|
56 |
+
"strip_accents": null,
|
57 |
+
"tokenize_chinese_chars": true,
|
58 |
+
"tokenizer_class": "BertTokenizer",
|
59 |
+
"truncation_side": "right",
|
60 |
+
"truncation_strategy": "longest_first",
|
61 |
+
"unk_token": "[UNK]"
|
62 |
+
}
|
vocab.txt
ADDED
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|
|