Push model using huggingface_hub.

1_Pooling/config.json

@@ -0,0 +1,9 @@
+{
+  "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
+}

README.md

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+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: Who was Cleopatra? She was a queen of ancient Egypt.
+- text: Did you go anywhere interesting this weekend? Yes, I went to the zoo.
+- text: Can robots think like humans? Not exactly, but AI can mimic some thinking
+    processes.
+- text: Can you name an adjective? 'Quick' is an adjective because it describes.
+- text: How does the water cycle work? Water evaporates, condenses into clouds, and
+    then precipitates back to the ground.
+pipeline_tag: text-classification
+inference: true
+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 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:** 7 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                                                                                                                                                                                                                                                                                                                                                                            |
+|:-----------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| English    | <ul><li>"Can you tell me about your favorite book? I love 'Harry Potter' because it's full of magic and adventure."</li><li>'What did you learn about poems today? We learned about rhymes and how they create a rhythm in poems.'</li><li>"Can you make a sentence using the word 'enigmatic'? The old man's smile was enigmatic, making me wonder what secrets he hid."</li></ul> |
+| Math       | <ul><li>"What is 8 times 9? It's 72."</li><li>'How do you find the area of a rectangle? Multiply the length by the width.'</li><li>"What's the difference between a prime number and a composite number? A prime number has only two factors, 1 and itself, while a composite number has more than two factors."</li></ul>                                                          |
+| Art        | <ul><li>'What colors do you mix to make green? Yellow and blue make green.'</li><li>'Who painted the Mona Lisa? Leonardo da Vinci painted it.'</li><li>"What's the difference between sculpture and pottery? Sculpture is the art of making figures while pottery is specifically making vessels from clay."</li></ul>                                                              |
+| Science    | <ul><li>"What is photosynthesis? It's the process by which plants make their food using sunlight."</li><li>'Can you name the planets in our solar system? Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune.'</li><li>"What's the difference between a solid and a liquid? A solid has a fixed shape while a liquid takes the shape of its container."</li></ul>    |
+| History    | <ul><li>'Who was the first president of the United States? George Washington was the first president.'</li><li>'Can you tell me about the Egyptian pyramids? They were massive tombs built for pharaohs, the biggest is the Pyramid of Giza.'</li><li>'What was the Renaissance? It was a period of great cultural and scientific advancement in Europe.'</li></ul>                 |
+| Technology | <ul><li>"What is the Internet? It's a global network of computers that can share information."</li><li>'Can you name a famous computer scientist? Alan Turing is known as one of the fathers of computer science.'</li><li>"What does 'AI' stand for? It stands for Artificial Intelligence."</li></ul>                                                                             |
+| NONE       | <ul><li>'What did you have for lunch today? I had a sandwich and some fruit.'</li><li>'Do you like playing outside? Yes, I love playing soccer with my friends.'</li><li>"What's your favorite TV show? I love watching 'SpongeBob SquarePants'."</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("bew/setfit-subject-model-basic")
+# Run inference
+preds = model("Who was Cleopatra? She was a queen of ancient Egypt.")
+```
+
+<!--
+### 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   | 6   | 14.1333 | 30  |
+
+| Label      | Training Sample Count |
+|:-----------|:----------------------|
+| Art        | 10                    |
+| English    | 10                    |
+| History    | 10                    |
+| Math       | 10                    |
+| NONE       | 15                    |
+| Science    | 10                    |
+| Technology | 10                    |
+
+### Training Hyperparameters
+- batch_size: (32, 32)
+- num_epochs: (10, 10)
+- max_steps: -1
+- 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
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0067 | 1    | 0.1987        | -               |
+| 0.3333 | 50   | 0.1814        | -               |
+| 0.6667 | 100  | 0.128         | -               |
+| 1.0    | 150  | 0.0146        | -               |
+| 1.3333 | 200  | 0.006         | -               |
+| 1.6667 | 250  | 0.0037        | -               |
+| 2.0    | 300  | 0.0031        | -               |
+| 2.3333 | 350  | 0.0027        | -               |
+| 2.6667 | 400  | 0.0024        | -               |
+| 3.0    | 450  | 0.0024        | -               |
+| 3.3333 | 500  | 0.002         | -               |
+| 3.6667 | 550  | 0.002         | -               |
+| 4.0    | 600  | 0.0017        | -               |
+| 4.3333 | 650  | 0.0019        | -               |
+| 4.6667 | 700  | 0.0018        | -               |
+| 5.0    | 750  | 0.0014        | -               |
+| 5.3333 | 800  | 0.0013        | -               |
+| 5.6667 | 850  | 0.0014        | -               |
+| 6.0    | 900  | 0.0014        | -               |
+| 6.3333 | 950  | 0.0014        | -               |
+| 6.6667 | 1000 | 0.0016        | -               |
+| 7.0    | 1050 | 0.0013        | -               |
+| 7.3333 | 1100 | 0.0013        | -               |
+| 7.6667 | 1150 | 0.0012        | -               |
+| 8.0    | 1200 | 0.0014        | -               |
+| 8.3333 | 1250 | 0.001         | -               |
+| 8.6667 | 1300 | 0.0012        | -               |
+| 9.0    | 1350 | 0.0014        | -               |
+| 9.3333 | 1400 | 0.0012        | -               |
+| 9.6667 | 1450 | 0.0012        | -               |
+| 10.0   | 1500 | 0.0011        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.3.1
+- Transformers: 4.35.2
+- PyTorch: 2.1.0+cu121
+- Datasets: 2.17.0
+- Tokenizers: 0.15.2
+
+## 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": "4.35.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.28.1",
+    "pytorch": "1.13.0+cu117"
+  }
+}

config_setfit.json

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+{
+  "normalize_embeddings": false,
+  "labels": [
+    "Art",
+    "English",
+    "History",
+    "Math",
+    "NONE",
+    "Science",
+    "Technology"
+  ]
+}

model.safetensors

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+oid sha256:70e7ceb3c783d84186da0b847beb5192d78bc274b2ede2ef5ea62c4b661216ed
+size 133462128

model_head.pkl

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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

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

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

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
+    },
+    "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,
+  "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]"
+}