Duplicate from vishnun/codenlbert-tiny

Co-authored-by: Vishnu Nandakumar <vishnun@users.noreply.huggingface.co>

.gitattributes

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.gitignore

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+checkpoint-*/

README.md

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+---
+license: mit
+datasets:
+- vishnun/CodevsNL
+language:
+- en
+metrics:
+- accuracy
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+- code
+- nli
+---
+
+## PreFace
+Code vs Natural language classification using bert-small from prajwall, below are the metrics achieved
+
+## Training Metrics
+
+| Epoch | Training Loss | Validation Loss | Accuracy |
+|-------|---------------|-----------------|----------|
+| 1     | 0.022500      | 0.012705        | 0.997203 |
+| 2     | 0.008700      | 0.013107        | 0.996880 |
+| 3     | 0.002700      | 0.014081        | 0.997633 |
+| 4     | 0.001800      | 0.010666        | 0.997526 |
+| 5     | 0.000900      | 0.010800        | 0.998063 |
+
+## More
+
+- Github repo for installable python package: https://github.com/Vishnunkumar
+- Space on the extraction of code blocks from screenshots: https://huggingface.co/spaces/vishnun/SnapCode

config.json

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+{
+  "_name_or_path": "prajjwal1/bert-tiny",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 128,
+  "id2label": {
+    "0": "CODE",
+    "1": "NL"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 512,
+  "label2id": {
+    "CODE": 0,
+    "NL": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 2,
+  "num_hidden_layers": 2,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.30.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
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+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

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