Add SetFit model

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 have owned this NAS for almost a year now and actually purchased a second
+    one It works flawlessly and QNAP live tech support is superb There is also a fairly
+    comprehensive forum for users as well I have slowly upgraded my capacities as
+    newer larger capacity drives have come out on the market All have been recognized
+    and the space expanded without a hitch I highly recommend this product '
+- text: Good as expected
+- text: 'This is a very good video editing package In the past I ve only used Corel
+    video editing products but Cyberlink s offering is on par It offers similar options
+    but they are different enough for me to want to use both products depending on
+    what I m trying to achieve There are quick uploading options that make it very
+    easy to get video onto Youtube and other online video sites '
+- text: Works great
+- text: 'This is my favorite crack open the computer and amuse myself for a few hours
+    software Easy to pick up if you have no prior experience with computer animation
+    but advanced enough that someone with the right skills could pull together an
+    impressive movie '
+inference: true
+---
+
+# 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:** 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                                                                                                                                                                                                                                                                                                                                                                                                                               |
+|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0     | <ul><li>'Have used Turbo Tax for years Never a problem I m pretty concerned now with the news that many of their users had their returns hacked by people who gained access to Turbo Tax and stole the information Not sure I will use it next year until I research how serious this is was '</li><li>'Can t beat an Apple computer Like P KB best by test '</li><li>'Works for Mac or Pc but not on widows '</li></ul>               |
+| 1     | <ul><li>'Would not install activation code not accepted Returned it '</li><li>'Worth all four of the software programs which are included in this product '</li><li>'The marketing information makes this software look like it should be fabulous lots of useful features that I would love to experiment with However the software just doesn t work I will keep using my very old JASC version of this software instead '</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("selina09/yt_setfit")
+# Run inference
+preds = model("Works great")
+```
+
+<!--
+### 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   | 34.9207 | 102 |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 123                   |
+| 1     | 41                    |
+
+### 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0019 | 1    | 0.2503        | -               |
+| 0.0942 | 50   | 0.2406        | -               |
+| 0.1883 | 100  | 0.2029        | -               |
+| 0.2825 | 150  | 0.2207        | -               |
+| 0.3766 | 200  | 0.1612        | -               |
+| 0.4708 | 250  | 0.0725        | -               |
+| 0.5650 | 300  | 0.0163        | -               |
+| 0.6591 | 350  | 0.0108        | -               |
+| 0.7533 | 400  | 0.0153        | -               |
+| 0.8475 | 450  | 0.0486        | -               |
+| 0.9416 | 500  | 0.0191        | -               |
+| 1.0358 | 550  | 0.0207        | -               |
+| 1.1299 | 600  | 0.0148        | -               |
+| 1.2241 | 650  | 0.0031        | -               |
+| 1.3183 | 700  | 0.001         | -               |
+| 1.4124 | 750  | 0.0287        | -               |
+| 1.5066 | 800  | 0.0146        | -               |
+| 1.6008 | 850  | 0.0147        | -               |
+| 1.6949 | 900  | 0.0165        | -               |
+| 1.7891 | 950  | 0.0008        | -               |
+| 1.8832 | 1000 | 0.0165        | -               |
+| 1.9774 | 1050 | 0.0007        | -               |
+| 2.0716 | 1100 | 0.0129        | -               |
+| 2.1657 | 1150 | 0.0143        | -               |
+| 2.2599 | 1200 | 0.0006        | -               |
+| 2.3540 | 1250 | 0.0008        | -               |
+| 2.4482 | 1300 | 0.0047        | -               |
+| 2.5424 | 1350 | 0.0005        | -               |
+| 2.6365 | 1400 | 0.0116        | -               |
+| 2.7307 | 1450 | 0.0093        | -               |
+| 2.8249 | 1500 | 0.0211        | -               |
+| 2.9190 | 1550 | 0.0076        | -               |
+| 3.0132 | 1600 | 0.0047        | -               |
+| 3.1073 | 1650 | 0.0005        | -               |
+| 3.2015 | 1700 | 0.0064        | -               |
+| 3.2957 | 1750 | 0.014         | -               |
+| 3.3898 | 1800 | 0.0479        | -               |
+| 3.4840 | 1850 | 0.0005        | -               |
+| 3.5782 | 1900 | 0.0045        | -               |
+| 3.6723 | 1950 | 0.0188        | -               |
+| 3.7665 | 2000 | 0.0004        | -               |
+| 3.8606 | 2050 | 0.0122        | -               |
+| 3.9548 | 2100 | 0.0004        | -               |
+| 4.0490 | 2150 | 0.008         | -               |
+| 4.1431 | 2200 | 0.0245        | -               |
+| 4.2373 | 2250 | 0.005         | -               |
+| 4.3315 | 2300 | 0.0244        | -               |
+| 4.4256 | 2350 | 0.0208        | -               |
+| 4.5198 | 2400 | 0.0237        | -               |
+| 4.6139 | 2450 | 0.0005        | -               |
+| 4.7081 | 2500 | 0.0004        | -               |
+| 4.8023 | 2550 | 0.02          | -               |
+| 4.8964 | 2600 | 0.0004        | -               |
+| 4.9906 | 2650 | 0.0067        | -               |
+| 5.0847 | 2700 | 0.0099        | -               |
+| 5.1789 | 2750 | 0.0138        | -               |
+| 5.2731 | 2800 | 0.0192        | -               |
+| 5.3672 | 2850 | 0.0217        | -               |
+| 5.4614 | 2900 | 0.0056        | -               |
+| 5.5556 | 2950 | 0.0003        | -               |
+| 5.6497 | 3000 | 0.0052        | -               |
+| 5.7439 | 3050 | 0.0123        | -               |
+| 5.8380 | 3100 | 0.0136        | -               |
+| 5.9322 | 3150 | 0.0221        | -               |
+| 6.0264 | 3200 | 0.0235        | -               |
+| 6.1205 | 3250 | 0.0144        | -               |
+| 6.2147 | 3300 | 0.0174        | -               |
+| 6.3089 | 3350 | 0.007         | -               |
+| 6.4030 | 3400 | 0.0044        | -               |
+| 6.4972 | 3450 | 0.0003        | -               |
+| 6.5913 | 3500 | 0.007         | -               |
+| 6.6855 | 3550 | 0.0004        | -               |
+| 6.7797 | 3600 | 0.0384        | -               |
+| 6.8738 | 3650 | 0.0055        | -               |
+| 6.9680 | 3700 | 0.0056        | -               |
+| 7.0621 | 3750 | 0.0118        | -               |
+| 7.1563 | 3800 | 0.0143        | -               |
+| 7.2505 | 3850 | 0.0289        | -               |
+| 7.3446 | 3900 | 0.0301        | -               |
+| 7.4388 | 3950 | 0.0119        | -               |
+| 7.5330 | 4000 | 0.012         | -               |
+| 7.6271 | 4050 | 0.0138        | -               |
+| 7.7213 | 4100 | 0.0148        | -               |
+| 7.8154 | 4150 | 0.0003        | -               |
+| 7.9096 | 4200 | 0.0268        | -               |
+| 8.0038 | 4250 | 0.0131        | -               |
+| 8.0979 | 4300 | 0.0237        | -               |
+| 8.1921 | 4350 | 0.0004        | -               |
+| 8.2863 | 4400 | 0.0211        | -               |
+| 8.3804 | 4450 | 0.0092        | -               |
+| 8.4746 | 4500 | 0.005         | -               |
+| 8.5687 | 4550 | 0.0056        | -               |
+| 8.6629 | 4600 | 0.0168        | -               |
+| 8.7571 | 4650 | 0.0045        | -               |
+| 8.8512 | 4700 | 0.0184        | -               |
+| 8.9454 | 4750 | 0.0049        | -               |
+| 9.0395 | 4800 | 0.0047        | -               |
+| 9.1337 | 4850 | 0.0099        | -               |
+| 9.2279 | 4900 | 0.0054        | -               |
+| 9.3220 | 4950 | 0.0185        | -               |
+| 9.4162 | 5000 | 0.005         | -               |
+| 9.5104 | 5050 | 0.0004        | -               |
+| 9.6045 | 5100 | 0.013         | -               |
+| 9.6987 | 5150 | 0.0002        | -               |
+| 9.7928 | 5200 | 0.0187        | -               |
+| 9.8870 | 5250 | 0.0003        | -               |
+| 9.9812 | 5300 | 0.0081        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 3.0.1
+- Transformers: 4.40.2
+- PyTorch: 2.4.0+cu121
+- Datasets: 2.21.0
+- 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

@@ -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": "4.40.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.40.2",
+    "pytorch": "2.4.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b49a023a593684036510e052dfc9090ef97a54ed91171990054ac35ecee17e0b
+size 133462128

model_head.pkl

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+oid sha256:613128c6dc5503ba761c362160ad96b673bd5694606d38a7fcff84e48fe1423e
+size 3935

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

@@ -0,0 +1,57 @@
+{
+  "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
+    },
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+      "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]"
+}