Push model using huggingface_hub.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: I was charged twice for the same service on my last billing cycle. Can you
+    help me confirm this?
+- text: Can you tell me if the ATM is available 24/7 at this location?
+- text: Can you explain how my account balance affects the service fees I’m being
+    charged?
+- text: I need to know the outstanding amount on my education loan.
+- text: Can you break down how interest rates affect my monthly payments for a home
+    equity loan?
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+datasets:
+- rbojja/labelled_bank_support_dataset
+base_model: BAAI/bge-small-en-v1.5
+model-index:
+- name: SetFit with BAAI/bge-small-en-v1.5
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: rbojja/labelled_bank_support_dataset
+      type: rbojja/labelled_bank_support_dataset
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.8640988372093024
+      name: Accuracy
+---
+
+# SetFit with BAAI/bge-small-en-v1.5
+
+This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [rbojja/labelled_bank_support_dataset](https://huggingface.co/datasets/rbojja/labelled_bank_support_dataset) dataset that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 10 classes
+- **Training Dataset:** [rbojja/labelled_bank_support_dataset](https://huggingface.co/datasets/rbojja/labelled_bank_support_dataset)
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+### Model Labels
+| Label | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
+|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1     | <ul><li>'Can you explain the differences between fixed and variable interest rates for personal loans?'</li><li>'Can you clarify why I was charged a fee for my account this month?'</li><li>'How can I access your customer service features if I need assistance?'</li></ul>                                                                                                                                                                                                   |
+| 2     | <ul><li>'Could you please verify that my deposit cancellation has been completed?'</li><li>"I've noticed a discrepancy in my balance after my latest deposit. Can you confirm if it was processed correctly?"</li><li>'Could you verify that my recent payment has been reversed successfully?'</li></ul>                                                                                                                                                                        |
+| 3     | <ul><li>"How do changes in the central bank's interest rates affect the interest I earn on my savings account?"</li><li>'Can I share my success story about earning rewards for referring friends? The incentives really helped me and I think others should know!'</li><li>'After my recent loan denial, how can I strengthen my reapplication to improve my approval odds?'</li></ul>                                                                                          |
+| 10    | <ul><li>"I just made a payment, but I'm not sure if it's been processed yet. Can you check for me?"</li><li>'I think there might be an error with my account balance; can you show me my most recent transactions?'</li><li>'I expected my payment to be completed by now, can you check the status for me?'</li></ul>                                                                                                                                                           |
+| 9     | <ul><li>'Can you please pass on my thanks to the customer service representative who helped me with my account security concerns? Their support made all the difference!'</li><li>'I just wanted to say thank you for the quick help with my issue!'</li><li>'I want to express my appreciation for the security alerts I received. It’s nice to know my account is being protected so well!'</li></ul>                                                                          |
+| 4     | <ul><li>"I'm ending my account with you; can you provide a summary of my final transaction?"</li></ul>                                                                                                                                                                                                                                                                                                                                                                           |
+| 7     | <ul><li>'Can I leave a review about my downgrade process? I have some suggestions for improvement.'</li><li>'Can you send me a notification of all my completed payments for this month?'</li><li>'Can you tell me how I can benefit from any investment opportunities with your bank?'</li></ul>                                                                                                                                                                                |
+| 6     | <ul><li>'I just checked my banking statement and I don’t recognize this last charge. How can I contest that?'</li><li>"I believe I've been incorrectly charged for a subscription service. What steps do I take?"</li><li>'I appreciate the service you provide, but the support response time during the outage was unacceptable.'</li></ul>                                                                                                                                    |
+| 0     | <ul><li>'I’m sorry, but I need to check on a transaction that was denied because of insufficient funds. Can you help me resolve this situation?'</li><li>"I'm really sorry, but I still can't access my account even after resetting my password. What should I do next?"</li><li>"I've been mistakenly locked out of my account and I feel bad about it. Can you assist me in regaining access?"</li></ul>                                                                      |
+| 8     | <ul><li>'I recently used your services and I’m really satisfied. Is there a way to share my thoughts on my overall banking experience?'</li><li>'I think it would be great if final account statements could be sent out at the beginning of the month instead of the end. It allows for better planning and review.'</li><li>'I wanted to share that I found the loan application submission very straightforward. When can I expect to hear back about my approval?'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.8641   |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import SetFitModel
+
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("rbojja/ft-intent-bank")
+# Run inference
+preds = model("I need to know the outstanding amount on my education loan.")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set | Min | Median | Max |
+|:-------------|:----|:-------|:----|
+| Word count   | 7   | 15.322 | 31  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 7                     |
+| 1     | 797                   |
+| 2     | 29                    |
+| 3     | 18                    |
+| 4     | 1                     |
+| 6     | 15                    |
+| 7     | 7                     |
+| 8     | 6                     |
+| 9     | 63                    |
+| 10    | 57                    |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 16)
+- max_steps: 3450
+- sampling_strategy: oversampling
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- l2_weight: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0003 | 1    | 0.2179        | -               |
+| 0.0145 | 50   | 0.2598        | -               |
+| 0.0290 | 100  | 0.2349        | -               |
+| 0.0435 | 150  | 0.2019        | -               |
+| 0.0580 | 200  | 0.1686        | -               |
+| 0.0725 | 250  | 0.1375        | -               |
+| 0.0870 | 300  | 0.1265        | -               |
+| 0.1014 | 350  | 0.0954        | -               |
+| 0.1159 | 400  | 0.0794        | -               |
+| 0.1304 | 450  | 0.065         | -               |
+| 0.1449 | 500  | 0.0731        | -               |
+| 0.1594 | 550  | 0.0547        | -               |
+| 0.1739 | 600  | 0.043         | -               |
+| 0.1884 | 650  | 0.0327        | -               |
+| 0.2029 | 700  | 0.027         | -               |
+| 0.2174 | 750  | 0.0285        | -               |
+| 0.2319 | 800  | 0.0201        | -               |
+| 0.2464 | 850  | 0.0151        | -               |
+| 0.2609 | 900  | 0.0131        | -               |
+| 0.2754 | 950  | 0.0076        | -               |
+| 0.2899 | 1000 | 0.0147        | -               |
+| 0.3043 | 1050 | 0.0122        | -               |
+| 0.3188 | 1100 | 0.0109        | -               |
+| 0.3333 | 1150 | 0.0126        | -               |
+| 0.3478 | 1200 | 0.0108        | -               |
+| 0.3623 | 1250 | 0.009         | -               |
+| 0.3768 | 1300 | 0.0072        | -               |
+| 0.3913 | 1350 | 0.0051        | -               |
+| 0.4058 | 1400 | 0.0057        | -               |
+| 0.4203 | 1450 | 0.0056        | -               |
+| 0.4348 | 1500 | 0.0079        | -               |
+| 0.4493 | 1550 | 0.0076        | -               |
+| 0.4638 | 1600 | 0.0029        | -               |
+| 0.4783 | 1650 | 0.0039        | -               |
+| 0.4928 | 1700 | 0.003         | -               |
+| 0.5072 | 1750 | 0.0037        | -               |
+| 0.5217 | 1800 | 0.0022        | -               |
+| 0.5362 | 1850 | 0.0032        | -               |
+| 0.5507 | 1900 | 0.0034        | -               |
+| 0.5652 | 1950 | 0.006         | -               |
+| 0.5797 | 2000 | 0.0046        | -               |
+| 0.5942 | 2050 | 0.0026        | -               |
+| 0.6087 | 2100 | 0.0031        | -               |
+| 0.6232 | 2150 | 0.0041        | -               |
+| 0.6377 | 2200 | 0.0049        | -               |
+| 0.6522 | 2250 | 0.0015        | -               |
+| 0.6667 | 2300 | 0.0053        | -               |
+| 0.6812 | 2350 | 0.0033        | -               |
+| 0.6957 | 2400 | 0.0055        | -               |
+| 0.7101 | 2450 | 0.0044        | -               |
+| 0.7246 | 2500 | 0.0036        | -               |
+| 0.7391 | 2550 | 0.0038        | -               |
+| 0.7536 | 2600 | 0.0038        | -               |
+| 0.7681 | 2650 | 0.0027        | -               |
+| 0.7826 | 2700 | 0.0028        | -               |
+| 0.7971 | 2750 | 0.0038        | -               |
+| 0.8116 | 2800 | 0.0033        | -               |
+| 0.8261 | 2850 | 0.0035        | -               |
+| 0.8406 | 2900 | 0.002         | -               |
+| 0.8551 | 2950 | 0.0034        | -               |
+| 0.8696 | 3000 | 0.0053        | -               |
+| 0.8841 | 3050 | 0.0035        | -               |
+| 0.8986 | 3100 | 0.0016        | -               |
+| 0.9130 | 3150 | 0.0021        | -               |
+| 0.9275 | 3200 | 0.0021        | -               |
+| 0.9420 | 3250 | 0.005         | -               |
+| 0.9565 | 3300 | 0.0031        | -               |
+| 0.9710 | 3350 | 0.0038        | -               |
+| 0.9855 | 3400 | 0.0029        | -               |
+| 1.0    | 3450 | 0.0019        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.1
+- Sentence Transformers: 3.3.1
+- Transformers: 4.47.1
+- PyTorch: 2.5.1+cu124
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+    doi = {10.48550/ARXIV.2209.11055},
+    url = {https://arxiv.org/abs/2209.11055},
+    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {Efficient Few-Shot Learning Without Prompts},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
+  "_name_or_path": "BAAI/bge-small-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
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+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
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+  "label2id": {
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+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.47.1",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+  "normalize_embeddings": false,
+  "labels": null
+}

model.safetensors

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model_head.pkl

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modules.json

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+    "type": "sentence_transformers.models.Transformer"
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+    "type": "sentence_transformers.models.Pooling"
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+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

special_tokens_map.json

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+}

tokenizer_config.json

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+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}