Add SetFit model

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

README.md

@@ -0,0 +1,263 @@
+---
+language:
+- en
+license: apache-2.0
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+datasets:
+- sst2
+metrics:
+- precision
+- recall
+- f1
+widget:
+- text: 'this is a story of two misfits who do n''t stand a chance alone , but together
+    they are magnificent . '
+- text: 'it does n''t believe in itself , it has no sense of humor ... it ''s just
+    plain bored . '
+- text: 'the band ''s courage in the face of official repression is inspiring , especially
+    for aging hippies ( this one included ) . '
+- text: 'a fast , funny , highly enjoyable movie . '
+- text: 'the movie achieves as great an impact by keeping these thoughts hidden as
+    ... ( quills ) did by showing them . '
+pipeline_tag: text-classification
+co2_eq_emissions:
+  emissions: 2.768308759172054
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: false
+  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
+  ram_total_size: 31.777088165283203
+  hours_used: 0.072
+  hardware_used: 1 x NVIDIA GeForce RTX 3090
+base_model: sentence-transformers/all-MiniLM-L6-v2
+model-index:
+- name: SetFit with sentence-transformers/all-MiniLM-L6-v2 on sst2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: sst2
+      type: sst2
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.7512953367875648
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/all-MiniLM-L6-v2 on sst2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [sst2](https://huggingface.co/datasets/sst2) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) 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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
+- **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:** [sst2](https://huggingface.co/datasets/sst2)
+- **Language:** en
+- **License:** apache-2.0
+
+### 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                                                                                                                                                                               |
+|:---------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| negative | <ul><li>'a tough pill to swallow and '</li><li>'indignation '</li><li>'that the typical hollywood disregard for historical truth and realism is at work here '</li></ul>               |
+| positive | <ul><li>"a moving experience for people who have n't read the book "</li><li>'in the best possible senses of both those words '</li><li>'to serve the work especially well '</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.7513   |
+
+## 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 🤗 Hub
+model = SetFitModel.from_pretrained("tomaarsen/setfit-all-MiniLM-L6-v2-sst2-8-shot")
+# Run inference
+preds = model("a fast , funny , highly enjoyable movie . ")
+```
+<!--
+### 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   | 10.2812 | 36  |
+
+| Label    | Training Sample Count |
+|:---------|:----------------------|
+| negative | 32                    |
+| positive | 32                    |
+
+### 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
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch      | Step   | Training Loss | Validation Loss |
+|:----------:|:------:|:-------------:|:---------------:|
+| 0.0076     | 1      | 0.3787        | -               |
+| 0.0758     | 10     | 0.2855        | -               |
+| 0.1515     | 20     | 0.3458        | 0.29            |
+| 0.2273     | 30     | 0.2496        | -               |
+| 0.3030     | 40     | 0.2398        | 0.2482          |
+| 0.3788     | 50     | 0.2068        | -               |
+| 0.4545     | 60     | 0.2471        | 0.244           |
+| 0.5303     | 70     | 0.2053        | -               |
+| **0.6061** | **80** | **0.1802**    | **0.2361**      |
+| 0.6818     | 90     | 0.0767        | -               |
+| 0.7576     | 100    | 0.0279        | 0.2365          |
+| 0.8333     | 110    | 0.0192        | -               |
+| 0.9091     | 120    | 0.0095        | 0.2527          |
+| 0.9848     | 130    | 0.0076        | -               |
+| 1.0606     | 140    | 0.0082        | 0.2651          |
+| 1.1364     | 150    | 0.0068        | -               |
+| 1.2121     | 160    | 0.0052        | 0.2722          |
+| 1.2879     | 170    | 0.0029        | -               |
+| 1.3636     | 180    | 0.0042        | 0.273           |
+| 1.4394     | 190    | 0.0026        | -               |
+| 1.5152     | 200    | 0.0036        | 0.2761          |
+| 1.5909     | 210    | 0.0044        | -               |
+| 1.6667     | 220    | 0.0027        | 0.2796          |
+| 1.7424     | 230    | 0.0025        | -               |
+| 1.8182     | 240    | 0.0025        | 0.2817          |
+| 1.8939     | 250    | 0.003         | -               |
+| 1.9697     | 260    | 0.0026        | 0.2817          |
+| 2.0455     | 270    | 0.0035        | -               |
+| 2.1212     | 280    | 0.002         | 0.2816          |
+| 2.1970     | 290    | 0.0023        | -               |
+| 2.2727     | 300    | 0.0016        | 0.2821          |
+| 2.3485     | 310    | 0.0023        | -               |
+| 2.4242     | 320    | 0.0015        | 0.2838          |
+| 2.5        | 330    | 0.0014        | -               |
+| 2.5758     | 340    | 0.002         | 0.2842          |
+| 2.6515     | 350    | 0.002         | -               |
+| 2.7273     | 360    | 0.0013        | 0.2847          |
+| 2.8030     | 370    | 0.0009        | -               |
+| 2.8788     | 380    | 0.0018        | 0.2857          |
+| 2.9545     | 390    | 0.0016        | -               |
+
+* The bold row denotes the saved checkpoint.
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Carbon Emitted**: 0.003 kg of CO2
+- **Hours Used**: 0.072 hours
+
+### Training Hardware
+- **On Cloud**: No
+- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
+- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
+- **RAM Size**: 31.78 GB
+
+### Framework Versions
+- Python: 3.9.16
+- SetFit: 1.0.0.dev0
+- Sentence Transformers: 2.2.2
+- Transformers: 4.29.0
+- PyTorch: 1.13.1+cu117
+- Datasets: 2.15.0
+- Tokenizers: 0.13.3
+
+## 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 +1,26 @@
+{
+  "_name_or_path": "checkpoints\\step_80\\",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.29.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.6.1",
+    "pytorch": "1.8.1"
+  }
+}

config_setfit.json

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

model_head.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f9e9469425745e2df00e05a906930ecf15100782a6d9a7225809dcb85b568cc2
+size 3887

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"
+  }
+]

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:28307aa11df3ffd2adec2a1e787390c05ef12f7841f998f08923adf587c1ee49
+size 90891565

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "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]"
+}