Add SetFit model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "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
+base_model: mental/mental-bert-base-uncased
+metrics:
+- accuracy
+widget:
+- text: I am going through a divorce. He is extremely angry. He refuses to physically
+    assist me with our teenager daughter. I have no extended family support. Often
+    times, I feel overwhelmed, tired, and joyless. I feel out of control, sad and
+    depressed on a daily basis. I am just going through the motions of life every
+    day. I am in my mid-50s. I have almost 29 years on my job. How can I handle this?
+- text: Every winter I find myself getting sad because of the weather. How can I fight
+    this?
+- text: Adjusting to life after significant life changes
+- text: "I have so many issues to address. I have a history of sexual abuse, I’m a\
+    \ breast cancer survivor and I am a lifetime insomniac.    I have a long history\
+    \ of depression and I’m beginning to have anxiety. I have low self esteem but\
+    \ I’ve been happily married for almost 35 years.\n   I’ve never had counseling\
+    \ about any of this. Do I have too many issues to address in counseling?"
+- text: Planning a DIY home renovation project.
+pipeline_tag: text-classification
+inference: true
+model-index:
+- name: SetFit with mental/mental-bert-base-uncased
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.9882352941176471
+      name: Accuracy
+---
+
+# SetFit with mental/mental-bert-base-uncased
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mental/mental-bert-base-uncased](https://huggingface.co/mental/mental-bert-base-uncased) 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:** [mental/mental-bert-base-uncased](https://huggingface.co/mental/mental-bert-base-uncased)
+- **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:** 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| True  | <ul><li>'I have so many issues to address. I have a history of sexual abuse, I’m a breast cancer survivor and I am a lifetime insomniac.    I have a long history of depression and I’m beginning to have anxiety. I have low self esteem but I’ve been happily married for almost 35 years.\n   I’ve never had counseling about any of this. Do I have too many issues to address in counseling?'</li><li>'I have so many issues to address. I have a history of sexual abuse, I’m a breast cancer survivor and I am a lifetime insomniac.    I have a long history of depression and I’m beginning to have anxiety. I have low self esteem but I’ve been happily married for almost 35 years.\n   I’ve never had counseling about any of this. Do I have too many issues to address in counseling?'</li><li>'Experiencing extreme mood swings not related to external circumstances.'</li></ul> |
+| False | <ul><li>'Guide to learning a new language'</li><li>'Learning about the historical significance of the Silk Road.'</li><li>'Exploring historical landmarks in Europe'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.9882   |
+
+## 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("richie-ghost/setfit-mental-bert-base-uncased-MH-Topic-Check")
+# Run inference
+preds = model("Planning a DIY home renovation project.")
+```
+
+<!--
+### 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   | 4   | 33.7092 | 111 |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| True  | 138                   |
+| False | 58                    |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (3, 3)
+- 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: True
+
+### Training Results
+| Epoch   | Step     | Training Loss | Validation Loss |
+|:-------:|:--------:|:-------------:|:---------------:|
+| 0.0007  | 1        | 0.2132        | -               |
+| 0.0354  | 50       | 0.1508        | -               |
+| 0.0708  | 100      | 0.0193        | -               |
+| 0.1062  | 150      | 0.0075        | -               |
+| 0.1415  | 200      | 0.0025        | -               |
+| 0.1769  | 250      | 0.0009        | -               |
+| 0.2123  | 300      | 0.0003        | -               |
+| 0.2477  | 350      | 0.0005        | -               |
+| 0.2831  | 400      | 0.0004        | -               |
+| 0.3185  | 450      | 0.0004        | -               |
+| 0.3539  | 500      | 0.0002        | -               |
+| 0.3892  | 550      | 0.0004        | -               |
+| 0.4246  | 600      | 0.0001        | -               |
+| 0.4600  | 650      | 0.0003        | -               |
+| 0.4954  | 700      | 0.0001        | -               |
+| 0.5308  | 750      | 0.0001        | -               |
+| 0.5662  | 800      | 0.0001        | -               |
+| 0.6016  | 850      | 0.0002        | -               |
+| 0.6369  | 900      | 0.0001        | -               |
+| 0.6723  | 950      | 0.0001        | -               |
+| 0.7077  | 1000     | 0.0001        | -               |
+| 0.7431  | 1050     | 0.0           | -               |
+| 0.7785  | 1100     | 0.0001        | -               |
+| 0.8139  | 1150     | 0.0001        | -               |
+| 0.8493  | 1200     | 0.0001        | -               |
+| 0.8846  | 1250     | 0.0001        | -               |
+| 0.9200  | 1300     | 0.0001        | -               |
+| 0.9554  | 1350     | 0.0001        | -               |
+| 0.9908  | 1400     | 0.0001        | -               |
+| **1.0** | **1413** | **-**         | **0.017**       |
+| 1.0262  | 1450     | 0.0001        | -               |
+| 1.0616  | 1500     | 0.0001        | -               |
+| 1.0970  | 1550     | 0.0           | -               |
+| 1.1323  | 1600     | 0.0001        | -               |
+| 1.1677  | 1650     | 0.0001        | -               |
+| 1.2031  | 1700     | 0.0001        | -               |
+| 1.2385  | 1750     | 0.0           | -               |
+| 1.2739  | 1800     | 0.0001        | -               |
+| 1.3093  | 1850     | 0.0           | -               |
+| 1.3447  | 1900     | 0.0           | -               |
+| 1.3800  | 1950     | 0.0           | -               |
+| 1.4154  | 2000     | 0.0           | -               |
+| 1.4508  | 2050     | 0.0           | -               |
+| 1.4862  | 2100     | 0.0           | -               |
+| 1.5216  | 2150     | 0.0           | -               |
+| 1.5570  | 2200     | 0.0           | -               |
+| 1.5924  | 2250     | 0.0           | -               |
+| 1.6277  | 2300     | 0.0           | -               |
+| 1.6631  | 2350     | 0.0           | -               |
+| 1.6985  | 2400     | 0.0           | -               |
+| 1.7339  | 2450     | 0.0           | -               |
+| 1.7693  | 2500     | 0.0           | -               |
+| 1.8047  | 2550     | 0.0           | -               |
+| 1.8401  | 2600     | 0.0           | -               |
+| 1.8754  | 2650     | 0.0           | -               |
+| 1.9108  | 2700     | 0.0001        | -               |
+| 1.9462  | 2750     | 0.0           | -               |
+| 1.9816  | 2800     | 0.0           | -               |
+| 2.0     | 2826     | -             | 0.018           |
+| 2.0170  | 2850     | 0.0           | -               |
+| 2.0524  | 2900     | 0.0           | -               |
+| 2.0878  | 2950     | 0.0           | -               |
+| 2.1231  | 3000     | 0.0           | -               |
+| 2.1585  | 3050     | 0.0           | -               |
+| 2.1939  | 3100     | 0.0           | -               |
+| 2.2293  | 3150     | 0.0           | -               |
+| 2.2647  | 3200     | 0.0           | -               |
+| 2.3001  | 3250     | 0.0           | -               |
+| 2.3355  | 3300     | 0.0           | -               |
+| 2.3708  | 3350     | 0.0           | -               |
+| 2.4062  | 3400     | 0.0           | -               |
+| 2.4416  | 3450     | 0.0           | -               |
+| 2.4770  | 3500     | 0.0           | -               |
+| 2.5124  | 3550     | 0.0           | -               |
+| 2.5478  | 3600     | 0.0           | -               |
+| 2.5832  | 3650     | 0.0           | -               |
+| 2.6185  | 3700     | 0.0           | -               |
+| 2.6539  | 3750     | 0.0           | -               |
+| 2.6893  | 3800     | 0.0           | -               |
+| 2.7247  | 3850     | 0.0           | -               |
+| 2.7601  | 3900     | 0.0           | -               |
+| 2.7955  | 3950     | 0.0           | -               |
+| 2.8309  | 4000     | 0.0           | -               |
+| 2.8662  | 4050     | 0.0001        | -               |
+| 2.9016  | 4100     | 0.0           | -               |
+| 2.9370  | 4150     | 0.0           | -               |
+| 2.9724  | 4200     | 0.0001        | -               |
+| 3.0     | 4239     | -             | 0.0182          |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.7.0
+- Transformers: 4.40.0
+- PyTorch: 2.2.1+cu121
+- Datasets: 2.19.0
+- 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

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+{
+  "_name_or_path": "checkpoints/step_1413",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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": "4.40.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.7.0",
+    "transformers": "4.40.0",
+    "pytorch": "2.2.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

@@ -0,0 +1,7 @@
+{
+  "labels": [
+    "True",
+    "False"
+  ],
+  "normalize_embeddings": false
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:549dfae8798214e768e5ae3a38aee4d73a94218b12ff67d229af6bdbf8af5603
+size 437951328

model_head.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b43712c69e55c0ac2426b55586fa4fa54fb19b9f1003a78c859474c4062b428e
+size 7023

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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

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
+    },
+    "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_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}