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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+---
+base_model: BAAI/bge-small-en-v1.5
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: I've exhausted all my knowledge on this question
+- text: That's all I can offer for this question at this time
+- text: I believe user engagement and time spent on the platform for Spotify's success
+    are crucial. I also believe that it's crucial to focus on providing personalized
+    recommendations and a seamless user experience to keep users engaged. Anything
+    else that you would like me to consider or key points that I may have missed?
+- text: 'so, here''s the gist of my recommendation: we need to focus on three areas
+    - execution, marketing, and sales. with that I have captured my key approach here.
+    anything else you want me to address?'
+- text: Let me revisit something you mentioned earlier.
+inference: true
+model-index:
+- name: SetFit with BAAI/bge-small-en-v1.5
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.9054054054054054
+      name: Accuracy
+---
+
+# SetFit with BAAI/bge-small-en-v1.5
+
+This is a [SetFit](https://github.com/huggingface/setfit) model 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:** 4 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **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                                                                                                                                                                                                                   |
+|:----------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| none            | <ul><li>'I’ll need to think it over to elaborate on this question.'</li><li>'I think I will go to Disneyland.'</li><li>'I missed part of that; could you please rephrase it for me?'</li></ul>                             |
+| wrapup_question | <ul><li>"That's all for now in regards to this question"</li><li>"Do you have any other issues you'd like me to address?"</li><li>'Do you have any other questions related to this topic?'</li></ul>                       |
+| end_question    | <ul><li>"let's do some other more meaningful question"</li><li>"I think I've covered everything I needed to for this question"</li><li>'Ok, I am done answering this question'</li></ul>                                   |
+| next_question   | <ul><li>'Can you please provide me a different question?'</li><li>"I've given that question a lot of thought. What's next?"</li><li>"I hope I answered your question to your satisfaction. What's the next one?"</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.9054   |
+
+## 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("nksk/Intent_bge-small-en-v1.5_v5.0")
+# Run inference
+preds = model("Let me revisit something you mentioned earlier.")
+```
+
+<!--
+### 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   | 1   | 38.7075 | 1048 |
+
+| Label           | Training Sample Count |
+|:----------------|:----------------------|
+| end_question    | 56                    |
+| next_question   | 30                    |
+| none            | 157                   |
+| wrapup_question | 51                    |
+
+### Training Hyperparameters
+- batch_size: (32, 16)
+- num_epochs: (3, 10)
+- max_steps: -1
+- sampling_strategy: oversampling
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.0005
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: True
+- use_amp: True
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0006 | 1    | 0.2718        | -               |
+| 0.0290 | 50   | 0.2554        | -               |
+| 0.0580 | 100  | 0.2373        | -               |
+| 0.0870 | 150  | 0.2127        | -               |
+| 0.1160 | 200  | 0.1728        | -               |
+| 0.1450 | 250  | 0.1301        | -               |
+| 0.1740 | 300  | 0.0944        | -               |
+| 0.2030 | 350  | 0.0591        | -               |
+| 0.2320 | 400  | 0.0393        | -               |
+| 0.2610 | 450  | 0.0217        | -               |
+| 0.2900 | 500  | 0.013         | -               |
+| 0.3190 | 550  | 0.0111        | -               |
+| 0.3480 | 600  | 0.006         | -               |
+| 0.3770 | 650  | 0.0047        | -               |
+| 0.4060 | 700  | 0.0035        | -               |
+| 0.4350 | 750  | 0.004         | -               |
+| 0.4640 | 800  | 0.0022        | -               |
+| 0.4930 | 850  | 0.0019        | -               |
+| 0.5220 | 900  | 0.0017        | -               |
+| 0.5510 | 950  | 0.0014        | -               |
+| 0.5800 | 1000 | 0.0013        | -               |
+| 0.6090 | 1050 | 0.0013        | -               |
+| 0.6381 | 1100 | 0.0012        | -               |
+| 0.6671 | 1150 | 0.0011        | -               |
+| 0.6961 | 1200 | 0.001         | -               |
+| 0.7251 | 1250 | 0.0009        | -               |
+| 0.7541 | 1300 | 0.0009        | -               |
+| 0.7831 | 1350 | 0.0009        | -               |
+| 0.8121 | 1400 | 0.0008        | -               |
+| 0.8411 | 1450 | 0.0008        | -               |
+| 0.8701 | 1500 | 0.0008        | -               |
+| 0.8991 | 1550 | 0.0007        | -               |
+| 0.9281 | 1600 | 0.0008        | -               |
+| 0.9571 | 1650 | 0.0007        | -               |
+| 0.9861 | 1700 | 0.0007        | -               |
+| 1.0151 | 1750 | 0.0007        | -               |
+| 1.0441 | 1800 | 0.0006        | -               |
+| 1.0731 | 1850 | 0.0006        | -               |
+| 1.1021 | 1900 | 0.0006        | -               |
+| 1.1311 | 1950 | 0.0006        | -               |
+| 1.1601 | 2000 | 0.0006        | -               |
+| 1.1891 | 2050 | 0.0006        | -               |
+| 1.2181 | 2100 | 0.0006        | -               |
+| 1.2471 | 2150 | 0.0006        | -               |
+| 1.2761 | 2200 | 0.0005        | -               |
+| 1.3051 | 2250 | 0.0005        | -               |
+| 1.3341 | 2300 | 0.0005        | -               |
+| 1.3631 | 2350 | 0.0005        | -               |
+| 1.3921 | 2400 | 0.0005        | -               |
+| 1.4211 | 2450 | 0.0005        | -               |
+| 1.4501 | 2500 | 0.0005        | -               |
+| 1.4791 | 2550 | 0.0005        | -               |
+| 1.5081 | 2600 | 0.0005        | -               |
+| 1.5371 | 2650 | 0.0004        | -               |
+| 1.5661 | 2700 | 0.0005        | -               |
+| 1.5951 | 2750 | 0.0005        | -               |
+| 1.6241 | 2800 | 0.0004        | -               |
+| 1.6531 | 2850 | 0.0004        | -               |
+| 1.6821 | 2900 | 0.0004        | -               |
+| 1.7111 | 2950 | 0.0004        | -               |
+| 1.7401 | 3000 | 0.0004        | -               |
+| 1.7691 | 3050 | 0.0004        | -               |
+| 1.7981 | 3100 | 0.0004        | -               |
+| 1.8271 | 3150 | 0.0004        | -               |
+| 1.8561 | 3200 | 0.0004        | -               |
+| 1.8852 | 3250 | 0.0004        | -               |
+| 1.9142 | 3300 | 0.0004        | -               |
+| 1.9432 | 3350 | 0.0004        | -               |
+| 1.9722 | 3400 | 0.0004        | -               |
+| 2.0012 | 3450 | 0.0004        | -               |
+| 2.0302 | 3500 | 0.0003        | -               |
+| 2.0592 | 3550 | 0.0004        | -               |
+| 2.0882 | 3600 | 0.0004        | -               |
+| 2.1172 | 3650 | 0.0004        | -               |
+| 2.1462 | 3700 | 0.0004        | -               |
+| 2.1752 | 3750 | 0.0004        | -               |
+| 2.2042 | 3800 | 0.0004        | -               |
+| 2.2332 | 3850 | 0.0003        | -               |
+| 2.2622 | 3900 | 0.0003        | -               |
+| 2.2912 | 3950 | 0.0003        | -               |
+| 2.3202 | 4000 | 0.0003        | -               |
+| 2.3492 | 4050 | 0.0003        | -               |
+| 2.3782 | 4100 | 0.0003        | -               |
+| 2.4072 | 4150 | 0.0003        | -               |
+| 2.4362 | 4200 | 0.0003        | -               |
+| 2.4652 | 4250 | 0.0003        | -               |
+| 2.4942 | 4300 | 0.0003        | -               |
+| 2.5232 | 4350 | 0.0003        | -               |
+| 2.5522 | 4400 | 0.0003        | -               |
+| 2.5812 | 4450 | 0.0003        | -               |
+| 2.6102 | 4500 | 0.0003        | -               |
+| 2.6392 | 4550 | 0.0003        | -               |
+| 2.6682 | 4600 | 0.0003        | -               |
+| 2.6972 | 4650 | 0.0003        | -               |
+| 2.7262 | 4700 | 0.0003        | -               |
+| 2.7552 | 4750 | 0.0003        | -               |
+| 2.7842 | 4800 | 0.0003        | -               |
+| 2.8132 | 4850 | 0.0003        | -               |
+| 2.8422 | 4900 | 0.0003        | -               |
+| 2.8712 | 4950 | 0.0003        | -               |
+| 2.9002 | 5000 | 0.0003        | -               |
+| 2.9292 | 5050 | 0.0003        | -               |
+| 2.9582 | 5100 | 0.0003        | -               |
+| 2.9872 | 5150 | 0.0003        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0
+- Sentence Transformers: 3.0.1
+- Transformers: 4.44.2
+- PyTorch: 2.5.0+cu121
+- Datasets: 3.0.2
+- Tokenizers: 0.19.1
+
+## 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

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "BAAI/bge-small-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "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": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.5.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

@@ -0,0 +1,9 @@
+{
+  "normalize_embeddings": false,
+  "labels": [
+    "end_question",
+    "next_question",
+    "none",
+    "wrapup_question"
+  ]
+}

model.safetensors

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+oid sha256:cfe1f5122e70783f46e82bc107c3ddf9e640d8aad4e83e4bde3374a1deb66fdc
+size 133462128

model_head.pkl

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+size 13399

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}