Add SetFit model

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "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
+}

README.md

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+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: Guy Cecil, the former head of the Democratic Senatorial Campaign Committee
+    and now the boss of a leading Democratic super PAC, voiced his frustration with
+    the inadequacy of Franken’s apology on Twitter.
+- text: Attorney Stephen Le Brocq, who operates a law firm in the North Texas area
+    sums up the treatment of Guyger perfectly when he says that “The affidavit isn’t
+    written objectively, not at the slightest.
+- text: Phone This field is for validation purposes and should be left unchanged.
+- text: The Twitter suspension caught me by surprise.
+- text: Popular pages like The AntiMedia (2.1 million fans), The Free Thought Project
+    (3.1 million fans), Press for Truth (350K fans), Police the Police (1.9 million
+    fans), Cop Block (1.7 million fans), and Punk Rock Libertarians (125K fans) are
+    just a few of the ones which were unpublished.
+pipeline_tag: text-classification
+inference: false
+base_model: sentence-transformers/paraphrase-mpnet-base-v2
+model-index:
+- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.7083881146463319
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier 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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
+- **Classification head:** a OneVsRestClassifier instance
+- **Maximum Sequence Length:** 512 tokens
+<!-- - **Number of Classes:** Unknown -->
+<!-- - **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)
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.7084   |
+
+## 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("anismahmahi/doubt_repetition_with_noPropaganda_with_3_zeros_SetFit")
+# Run inference
+preds = model("The Twitter suspension caught me by surprise.")
+```
+
+<!--
+### 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   | 22.0291 | 129 |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (2, 2)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 5
+- 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 |
+|:-------:|:--------:|:-------------:|:---------------:|
+| 0.0003  | 1        | 0.3532        | -               |
+| 0.0166  | 50       | 0.3413        | -               |
+| 0.0332  | 100      | 0.2743        | -               |
+| 0.0498  | 150      | 0.2635        | -               |
+| 0.0664  | 200      | 0.2444        | -               |
+| 0.0830  | 250      | 0.1883        | -               |
+| 0.0996  | 300      | 0.2231        | -               |
+| 0.1162  | 350      | 0.1763        | -               |
+| 0.1328  | 400      | 0.1868        | -               |
+| 0.1494  | 450      | 0.2057        | -               |
+| 0.1660  | 500      | 0.1734        | -               |
+| 0.1826  | 550      | 0.2594        | -               |
+| 0.1992  | 600      | 0.1024        | -               |
+| 0.2158  | 650      | 0.2351        | -               |
+| 0.2324  | 700      | 0.1863        | -               |
+| 0.2490  | 750      | 0.072         | -               |
+| 0.2656  | 800      | 0.1987        | -               |
+| 0.2822  | 850      | 0.1511        | -               |
+| 0.2988  | 900      | 0.0926        | -               |
+| 0.3154  | 950      | 0.1956        | -               |
+| 0.3320  | 1000     | 0.1354        | -               |
+| 0.3486  | 1050     | 0.2038        | -               |
+| 0.3652  | 1100     | 0.1166        | -               |
+| 0.3818  | 1150     | 0.3214        | -               |
+| 0.3984  | 1200     | 0.0703        | -               |
+| 0.4150  | 1250     | 0.1815        | -               |
+| 0.4316  | 1300     | 0.124         | -               |
+| 0.4482  | 1350     | 0.0955        | -               |
+| 0.4648  | 1400     | 0.1064        | -               |
+| 0.4814  | 1450     | 0.0429        | -               |
+| 0.4980  | 1500     | 0.0814        | -               |
+| 0.5146  | 1550     | 0.1483        | -               |
+| 0.5312  | 1600     | 0.0856        | -               |
+| 0.5478  | 1650     | 0.1072        | -               |
+| 0.5644  | 1700     | 0.0148        | -               |
+| 0.5810  | 1750     | 0.0571        | -               |
+| 0.5976  | 1800     | 0.052         | -               |
+| 0.6142  | 1850     | 0.0532        | -               |
+| 0.6308  | 1900     | 0.0088        | -               |
+| 0.6474  | 1950     | 0.1619        | -               |
+| 0.6640  | 2000     | 0.0618        | -               |
+| 0.6806  | 2050     | 0.0115        | -               |
+| 0.6972  | 2100     | 0.1402        | -               |
+| 0.7138  | 2150     | 0.0637        | -               |
+| 0.7304  | 2200     | 0.0194        | -               |
+| 0.7470  | 2250     | 0.0135        | -               |
+| 0.7636  | 2300     | 0.0109        | -               |
+| 0.7802  | 2350     | 0.133         | -               |
+| 0.7968  | 2400     | 0.0565        | -               |
+| 0.8134  | 2450     | 0.1508        | -               |
+| 0.8300  | 2500     | 0.0293        | -               |
+| 0.8466  | 2550     | 0.065         | -               |
+| 0.8632  | 2600     | 0.0029        | -               |
+| 0.8798  | 2650     | 0.008         | -               |
+| 0.8964  | 2700     | 0.0604        | -               |
+| 0.9130  | 2750     | 0.0074        | -               |
+| 0.9296  | 2800     | 0.0019        | -               |
+| 0.9462  | 2850     | 0.0129        | -               |
+| 0.9628  | 2900     | 0.0838        | -               |
+| 0.9794  | 2950     | 0.0044        | -               |
+| 0.9960  | 3000     | 0.0035        | -               |
+| **1.0** | **3012** | **-**         | **0.2514**      |
+| 1.0126  | 3050     | 0.0086        | -               |
+| 1.0292  | 3100     | 0.0042        | -               |
+| 1.0458  | 3150     | 0.0833        | -               |
+| 1.0624  | 3200     | 0.058         | -               |
+| 1.0790  | 3250     | 0.013         | -               |
+| 1.0956  | 3300     | 0.0429        | -               |
+| 1.1122  | 3350     | 0.0044        | -               |
+| 1.1288  | 3400     | 0.0699        | -               |
+| 1.1454  | 3450     | 0.0535        | -               |
+| 1.1620  | 3500     | 0.0559        | -               |
+| 1.1786  | 3550     | 0.1459        | -               |
+| 1.1952  | 3600     | 0.118         | -               |
+| 1.2118  | 3650     | 0.14          | -               |
+| 1.2284  | 3700     | 0.0632        | -               |
+| 1.2450  | 3750     | 0.0026        | -               |
+| 1.2616  | 3800     | 0.0026        | -               |
+| 1.2782  | 3850     | 0.0052        | -               |
+| 1.2948  | 3900     | 0.0058        | -               |
+| 1.3114  | 3950     | 0.0018        | -               |
+| 1.3280  | 4000     | 0.0152        | -               |
+| 1.3446  | 4050     | 0.0186        | -               |
+| 1.3612  | 4100     | 0.039         | -               |
+| 1.3778  | 4150     | 0.0022        | -               |
+| 1.3944  | 4200     | 0.002         | -               |
+| 1.4110  | 4250     | 0.0032        | -               |
+| 1.4276  | 4300     | 0.0285        | -               |
+| 1.4442  | 4350     | 0.0213        | -               |
+| 1.4608  | 4400     | 0.0009        | -               |
+| 1.4774  | 4450     | 0.0262        | -               |
+| 1.4940  | 4500     | 0.0181        | -               |
+| 1.5106  | 4550     | 0.0629        | -               |
+| 1.5272  | 4600     | 0.0023        | -               |
+| 1.5438  | 4650     | 0.003         | -               |
+| 1.5604  | 4700     | 0.0024        | -               |
+| 1.5770  | 4750     | 0.049         | -               |
+| 1.5936  | 4800     | 0.0154        | -               |
+| 1.6102  | 4850     | 0.0009        | -               |
+| 1.6268  | 4900     | 0.0015        | -               |
+| 1.6434  | 4950     | 0.0068        | -               |
+| 1.6600  | 5000     | 0.057         | -               |
+| 1.6766  | 5050     | 0.0031        | -               |
+| 1.6932  | 5100     | 0.0189        | -               |
+| 1.7098  | 5150     | 0.0317        | -               |
+| 1.7264  | 5200     | 0.0013        | -               |
+| 1.7430  | 5250     | 0.0247        | -               |
+| 1.7596  | 5300     | 0.0062        | -               |
+| 1.7762  | 5350     | 0.0192        | -               |
+| 1.7928  | 5400     | 0.0019        | -               |
+| 1.8094  | 5450     | 0.1007        | -               |
+| 1.8260  | 5500     | 0.0384        | -               |
+| 1.8426  | 5550     | 0.0494        | -               |
+| 1.8592  | 5600     | 0.0615        | -               |
+| 1.8758  | 5650     | 0.0709        | -               |
+| 1.8924  | 5700     | 0.0308        | -               |
+| 1.9090  | 5750     | 0.0107        | -               |
+| 1.9256  | 5800     | 0.064         | -               |
+| 1.9422  | 5850     | 0.0009        | -               |
+| 1.9588  | 5900     | 0.0019        | -               |
+| 1.9754  | 5950     | 0.0037        | -               |
+| 1.9920  | 6000     | 0.0826        | -               |
+| 2.0     | 6024     | -             | 0.2614          |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.1
+- Sentence Transformers: 2.2.2
+- Transformers: 4.35.2
+- PyTorch: 2.1.0+cu121
+- Datasets: 2.16.1
+- Tokenizers: 0.15.0
+
+## 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 +1,24 @@
+{
+  "_name_or_path": "checkpoints/step_3012/",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

config_setfit.json

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

model.safetensors

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+oid sha256:9333be8da1b82b30bd75e028b0d06706b845ab588a09f81c02776b968ef109ba
+size 437967672

model_head.pkl

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+oid sha256:eaf7ecf315fb16b611c2f871e975ded468cf5c7ed0ee1ed49bb1ee4e97c658f2
+size 20436

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

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "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

@@ -0,0 +1,66 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "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
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}