Add SetFit model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1024,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "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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+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+- weighted precision
+- weighted recall
+- weighted f1
+- macro precision
+- macro recall
+- macro f1
+widget:
+- text: Roles can be assigned to a user account for individual products.
+- text: The number of active Subscription Versions in a sample to be monitored by
+    the NPAC SMS.
+- text: 'The visual representation of an SDT or a part of an SDT. '
+- text: Open Society Institute Guide to Institutional Repository Software, 3rd ed.
+    (2004)
+- text: 'The Application/Delete menu item shall provide an interface for deleting
+    an application and all the files in the application directory. '
+pipeline_tag: text-classification
+inference: true
+base_model: sentence-transformers/all-roberta-large-v1
+model-index:
+- name: SetFit with sentence-transformers/all-roberta-large-v1
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.7621000820344545
+      name: Accuracy
+    - type: weighted precision
+      value: 0.7627752679232598
+      name: Weighted Precision
+    - type: weighted recall
+      value: 0.7621000820344545
+      name: Weighted Recall
+    - type: weighted f1
+      value: 0.7621663772102192
+      name: Weighted F1
+    - type: macro precision
+      value: 0.7621734718049769
+      name: Macro Precision
+    - type: macro recall
+      value: 0.7624659767698817
+      name: Macro Recall
+    - type: macro f1
+      value: 0.7620481988534211
+      name: Macro F1
+---
+
+# SetFit with sentence-transformers/all-roberta-large-v1
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) 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:** [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 256 tokens
+- **Number of Classes:** 2 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1     | <ul><li>'The matrix dimensions are fixed, and are the same when displaying departments or categories.'</li><li>'The Clarus program shall provide for customer service.'</li><li>'NPAC SMS shall identify the originator of any accessible system resources.'</li></ul>                                                                                                                                                                                                                                                                                                                  |
+| 0     | <ul><li>'A search pattern is a string w such that w is a sub-string of a string α and α is a string derived from some non- terminal β in the target grammar.'</li><li>'Normally only one or two parties are engaged in operation and maintenance of the wind turbine(s), typically the owner and the operation and maintenance organisation, which in some cases is one and the same.'</li><li>'TASE-2 (ICCP) resides on layer 7 in the OSI-model and is an MMS companion standard, that is, the general MMS services have been particularised for telecontrol applications.'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy | Weighted Precision | Weighted Recall | Weighted F1 | Macro Precision | Macro Recall | Macro F1 |
+|:--------|:---------|:-------------------|:----------------|:------------|:----------------|:-------------|:---------|
+| **all** | 0.7621   | 0.7628             | 0.7621          | 0.7622      | 0.7622          | 0.7625       | 0.7620   |
+
+## 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("kwang123/roberta-large-setfit-ReqORNot")
+# Run inference
+preds = model("The visual representation of an SDT or a part of an SDT. ")
+```
+
+<!--
+### 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   | 5   | 21.7708 | 46  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 24                    |
+| 1     | 24                    |
+
+### Training Hyperparameters
+- batch_size: (8, 8)
+- 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    | 0.3795        | -               |
+| 0.3333 | 50   | 0.298         | -               |
+| 0.6667 | 100  | 0.0025        | -               |
+| 1.0    | 150  | 0.0002        | -               |
+| 1.3333 | 200  | 0.0002        | -               |
+| 1.6667 | 250  | 0.0001        | -               |
+| 2.0    | 300  | 0.0001        | -               |
+| 2.3333 | 350  | 0.0001        | -               |
+| 2.6667 | 400  | 0.0001        | -               |
+| 3.0    | 450  | 0.0001        | -               |
+| 3.3333 | 500  | 0.0           | -               |
+| 3.6667 | 550  | 0.0           | -               |
+| 4.0    | 600  | 0.0           | -               |
+| 4.3333 | 650  | 0.0001        | -               |
+| 4.6667 | 700  | 0.0           | -               |
+| 5.0    | 750  | 0.0           | -               |
+| 5.3333 | 800  | 0.0           | -               |
+| 5.6667 | 850  | 0.0           | -               |
+| 6.0    | 900  | 0.0           | -               |
+| 6.3333 | 950  | 0.0001        | -               |
+| 6.6667 | 1000 | 0.0           | -               |
+| 7.0    | 1050 | 0.0           | -               |
+| 7.3333 | 1100 | 0.0           | -               |
+| 7.6667 | 1150 | 0.0           | -               |
+| 8.0    | 1200 | 0.0           | -               |
+| 8.3333 | 1250 | 0.0           | -               |
+| 8.6667 | 1300 | 0.0           | -               |
+| 9.0    | 1350 | 0.0           | -               |
+| 9.3333 | 1400 | 0.0           | -               |
+| 9.6667 | 1450 | 0.0           | -               |
+| 10.0   | 1500 | 0.0           | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.5.1
+- Transformers: 4.38.1
+- PyTorch: 2.1.0+cu121
+- Datasets: 2.18.0
+- 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

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+{
+  "_name_or_path": "sentence-transformers/all-roberta-large-v1",
+  "architectures": [
+    "RobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.6.1",
+    "pytorch": "1.8.1"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

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

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ccd1771f575df48def0276547eb9491a96384ad4bc6b92ca095e7e034c6de415
+size 1421483904

model_head.pkl

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+oid sha256:e3976d4836cc85828dfc7f44f47268b230dc6f9bf69a5a417bcc93843c873c54
+size 9055

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": 256,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "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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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
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+      "special": true
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+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}