Add SetFit model

1_Pooling/config.json

@@ -1,7 +1,9 @@
 {
-  "word_embedding_dimension": 768,
+  "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
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false
 }

README.md

@@ -1,49 +1,131 @@
 ---
-license: apache-2.0
+library_name: setfit
 tags:
 - setfit
 - sentence-transformers
 - text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget: []
 pipeline_tag: text-classification
+inference: true
+base_model: sentence-transformers/all-MiniLM-L12-v1
 ---
 
-# emaadshehzad/setfit-DK-V1
+# SetFit with sentence-transformers/all-MiniLM-L12-v1
 
-This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L12-v1](https://huggingface.co/sentence-transformers/all-MiniLM-L12-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.
 
-## Usage
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L12-v1](https://huggingface.co/sentence-transformers/all-MiniLM-L12-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:** 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)
+
+## Uses
+
+### Direct Use for Inference
 
-To use this model for inference, first install the SetFit library:
+First install the SetFit library:
 
 ```bash
-python -m pip install setfit
+pip install setfit
 ```
 
-You can then run inference as follows:
+Then you can load this model and run inference.
 
 ```python
 from setfit import SetFitModel
 
-# Download from Hub and run inference
+# Download from the 🤗 Hub
 model = SetFitModel.from_pretrained("emaadshehzad/setfit-DK-V1")
 # Run inference
-preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
+preds = model("I loved the spiderman movie!")
 ```
 
-## BibTeX entry and citation info
+<!--
+### 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
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.3.1
+- Transformers: 4.35.2
+- PyTorch: 2.1.0+cu121
+- Datasets: 2.16.1
+- Tokenizers: 0.15.1
+
+## 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}
+    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

@@ -1,24 +1,26 @@
 {
-  "_name_or_path": "/root/.cache/torch/sentence_transformers/sentence-transformers_all-mpnet-base-v2/",
+  "_name_or_path": "sentence-transformers/all-MiniLM-L12-v1",
   "architectures": [
-    "MPNetModel"
+    "BertModel"
   ],
   "attention_probs_dropout_prob": 0.1,
-  "bos_token_id": 0,
-  "eos_token_id": 2,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
+  "hidden_size": 384,
   "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "layer_norm_eps": 1e-05,
-  "max_position_embeddings": 514,
-  "model_type": "mpnet",
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
   "num_attention_heads": 12,
   "num_hidden_layers": 12,
-  "pad_token_id": 1,
-  "relative_attention_num_buckets": 32,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
   "torch_dtype": "float32",
   "transformers_version": "4.35.2",
-  "vocab_size": 30527
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
 }

config_setfit.json

@@ -0,0 +1,4 @@
+{
+  "normalize_embeddings": false,
+  "labels": null
+}

model.safetensors

@@ -1,3 +1,3 @@
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+size 133462128

model_head.pkl

@@ -1,3 +1,3 @@
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-size 555599
+oid sha256:04aba599b084c7bae433b8d655507feecb3e3fd9ad67b7082120ac2316a38a0a
+size 272935

sentence_bert_config.json

@@ -1,4 +1,4 @@
 {
-  "max_seq_length": 384,
+  "max_seq_length": 256,
   "do_lower_case": false
 }

special_tokens_map.json

@@ -1,51 +1,7 @@
 {
-  "bos_token": {
-    "content": "<s>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "cls_token": {
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-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  },
-  "eos_token": {
-    "content": "</s>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "mask_token": {
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-    "lstrip": true,
-    "normalized": false,
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-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  },
-  "unk_token": {
-    "content": "[UNK]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  }
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
 }

tokenizer_config.json

@@ -1,71 +1,63 @@
 {
   "added_tokens_decoder": {
     "0": {
-      "content": "<s>",
+      "content": "[PAD]",
       "lstrip": false,
       "normalized": false,
       "rstrip": false,
       "single_word": false,
       "special": true
     },
-    "1": {
-      "content": "<pad>",
+    "100": {
+      "content": "[UNK]",
       "lstrip": false,
       "normalized": false,
       "rstrip": false,
       "single_word": false,
       "special": true
     },
-    "2": {
-      "content": "</s>",
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       "lstrip": false,
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       "special": true
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-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "104": {
-      "content": "[UNK]",
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       "lstrip": false,
       "normalized": false,
       "rstrip": false,
       "single_word": false,
       "special": true
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-    "30526": {
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-      "lstrip": true,
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+      "lstrip": false,
       "normalized": false,
       "rstrip": false,
       "single_word": false,
       "special": true
     }
   },
-  "bos_token": "<s>",
   "clean_up_tokenization_spaces": true,
-  "cls_token": "<s>",
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
   "do_lower_case": true,
-  "eos_token": "</s>",
-  "mask_token": "<mask>",
+  "mask_token": "[MASK]",
   "max_length": 128,
   "model_max_length": 512,
+  "never_split": null,
   "pad_to_multiple_of": null,
-  "pad_token": "<pad>",
+  "pad_token": "[PAD]",
   "pad_token_type_id": 0,
   "padding_side": "right",
-  "sep_token": "</s>",
+  "sep_token": "[SEP]",
   "stride": 0,
   "strip_accents": null,
   "tokenize_chinese_chars": true,
-  "tokenizer_class": "MPNetTokenizer",
+  "tokenizer_class": "BertTokenizer",
   "truncation_side": "right",
   "truncation_strategy": "longest_first",
   "unk_token": "[UNK]"

vocab.txt

@@ -1,7 +1,3 @@
-<s>
-<pad>
-</s>
-<unk>
 [PAD]
 [unused0]
 [unused1]
@@ -30524,4 +30520,3 @@ necessitated
 ##：
 ##？
 ##～
-<mask>