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

@@ -0,0 +1,246 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: '"I think this might be the solution."'
+- text: '"Oh no, I apologize!"'
+- text: Could you repeat that, please?
+- text: Oh, this is so disappointing.
+- text: Uhh, clear.
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+datasets:
+- rbojja/zero-shot-intent-classification
+base_model: BAAI/bge-small-en-v1.5
+---
+
+# SetFit with BAAI/bge-small-en-v1.5
+
+This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [rbojja/zero-shot-intent-classification](https://huggingface.co/datasets/rbojja/zero-shot-intent-classification) 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:** 18 classes
+- **Training Dataset:** [rbojja/zero-shot-intent-classification](https://huggingface.co/datasets/rbojja/zero-shot-intent-classification)
+<!-- - **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                                                                                                                                                 |
+|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 7     | <ul><li>'Oh my, this is great!'</li><li>'Oh, this is fantastic!'</li><li>'Hmm, I’m so delighted!'</li></ul>                                              |
+| 3     | <ul><li>"Oh, absolutely, that's it!"</li><li>"Oh, absolutely, that's it!"</li><li>"Yep, that's exactly what I meant."</li></ul>                          |
+| 15    | <ul><li>'Really, no way?'</li><li>'Oh, that’s quite something!'</li><li>'Oh, that’s quite something!'</li></ul>                                          |
+| 8     | <ul><li>"Gotcha... oh, that's clear!"</li><li>'Hmm, I see... perfect!'</li><li>'Oh, I see... clear!'</li></ul>                                           |
+| 12    | <ul><li>'Uhh, fine.'</li><li>'Oh, clear.'</li><li>'Uhh, noted.'</li></ul>                                                                                |
+| 9     | <ul><li>'Uhh, take care!'</li><li>'Hmm, see you!'</li><li>'Uhh, see you!'</li></ul>                                                                      |
+| 17    | <ul><li>'"Umm, this could be a decent plan."'</li><li>'"I think this might be the solution."'</li><li>'"Maybe this will work out, I suppose."'</li></ul> |
+| 0     | <ul><li>"Why can't you just work?!"</li><li>'Seriously, this is a joke!'</li><li>'Ugh, this is so frustrating!'</li></ul>                                |
+| 6     | <ul><li>'"Oh, what if I\'m a dream?"'</li><li>'"Oh, do you speak dolphin?"'</li><li>'"Uhh, do you have a wish?"'</li></ul>                               |
+| 11    | <ul><li>"Uh-huh, that's a valid point."</li><li>'Like, I get it.'</li><li>'Right, I understand.'</li></ul>                                               |
+| 16    | <ul><li>'Thank you!'</li><li>'"Hmmm, thanks, you\'re great!"'</li><li>'"Oh, fantastic, thanks a lot!"'</li></ul>                                         |
+| 4     | <ul><li>"Sorry, I'm not sure."</li><li>"Well, I'm lost."</li><li>"Hmm, I'm not sure."</li></ul>                                                          |
+| 10    | <ul><li>'Oh, hi!'</li><li>"Hello! What's new?"</li><li>"Hi! How's life?"</li></ul>                                                                       |
+| 13    | <ul><li>'Oh, gotcha.'</li><li>'Hmmm, okay.'</li><li>'Alright, thanks.'</li></ul>                                                                         |
+| 2     | <ul><li>'What’s the context behind that?'</li><li>'Could you simplify that for me?'</li><li>'Can you explain that concept?'</li></ul>                    |
+| 1     | <ul><li>'"Oh, I didn’t mean to."'</li><li>'"Oops, sorry for the oversight."'</li><li>'"Oops, I’m really sorry."'</li></ul>                               |
+| 5     | <ul><li>'Oh, this is not what I wanted.'</li><li>'Oh no, this is not right.'</li><li>'Seriously, this is a failure.'</li></ul>                           |
+| 14    | <ul><li>'Uhh, superb choice!'</li><li>'Uhh, amazing decision!'</li><li>'Oh, superb performance!'</li></ul>                                               |
+
+## 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/intent-classification-small")
+# Run inference
+preds = model("Uhh, clear.")
+```
+
+<!--
+### 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   | 2   | 4.2224 | 9   |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 40                    |
+| 1     | 40                    |
+| 2     | 37                    |
+| 3     | 40                    |
+| 4     | 41                    |
+| 5     | 38                    |
+| 6     | 42                    |
+| 7     | 38                    |
+| 8     | 35                    |
+| 9     | 39                    |
+| 10    | 42                    |
+| 11    | 41                    |
+| 12    | 42                    |
+| 13    | 44                    |
+| 14    | 38                    |
+| 15    | 43                    |
+| 16    | 47                    |
+| 17    | 37                    |
+
+### Training Hyperparameters
+- batch_size: (16, 2)
+- num_epochs: (1, 16)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 20
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0006 | 1    | 0.149         | -               |
+| 0.0276 | 50   | 0.1836        | -               |
+| 0.0552 | 100  | 0.1408        | -               |
+| 0.0829 | 150  | 0.0978        | -               |
+| 0.1105 | 200  | 0.0805        | -               |
+| 0.1381 | 250  | 0.0684        | -               |
+| 0.1657 | 300  | 0.0594        | -               |
+| 0.1934 | 350  | 0.051         | -               |
+| 0.2210 | 400  | 0.0383        | -               |
+| 0.2486 | 450  | 0.0379        | -               |
+| 0.2762 | 500  | 0.035         | -               |
+| 0.3039 | 550  | 0.0334        | -               |
+| 0.3315 | 600  | 0.0306        | -               |
+| 0.3591 | 650  | 0.0266        | -               |
+| 0.3867 | 700  | 0.0264        | -               |
+| 0.4144 | 750  | 0.018         | -               |
+| 0.4420 | 800  | 0.0193        | -               |
+| 0.4696 | 850  | 0.0166        | -               |
+| 0.4972 | 900  | 0.0165        | -               |
+| 0.5249 | 950  | 0.016         | -               |
+| 0.5525 | 1000 | 0.0177        | -               |
+| 0.5801 | 1050 | 0.0202        | -               |
+| 0.6077 | 1100 | 0.0133        | -               |
+| 0.6354 | 1150 | 0.014         | -               |
+| 0.6630 | 1200 | 0.013         | -               |
+| 0.6906 | 1250 | 0.0161        | -               |
+| 0.7182 | 1300 | 0.0119        | -               |
+| 0.7459 | 1350 | 0.0132        | -               |
+| 0.7735 | 1400 | 0.0131        | -               |
+| 0.8011 | 1450 | 0.0123        | -               |
+| 0.8287 | 1500 | 0.0115        | -               |
+| 0.8564 | 1550 | 0.0111        | -               |
+| 0.8840 | 1600 | 0.011         | -               |
+| 0.9116 | 1650 | 0.01          | -               |
+| 0.9392 | 1700 | 0.0098        | -               |
+| 0.9669 | 1750 | 0.0142        | -               |
+| 0.9945 | 1800 | 0.0132        | -               |
+
+### Framework Versions
+- Python: 3.11.11
+- SetFit: 1.1.1
+- Sentence Transformers: 3.3.1
+- Transformers: 4.47.1
+- PyTorch: 2.5.1+cu121
+- 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

@@ -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": "4.47.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.47.1",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

@@ -0,0 +1,4 @@
+{
+  "normalize_embeddings": false,
+  "labels": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:10aae658e3cab6ea00cd4eae39769d51c9427f62aee94794bc665c319f1607b7
+size 133462128

model_head.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ae45b37b0ebad7d90a40be70afa791ce398a699c2af10ae45445f4abbcdc8350
+size 56423

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "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

@@ -0,0 +1,58 @@
+{
+  "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
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "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,
+  "extra_special_tokens": {},
+  "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]"
+}