End of training

README.md

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+---
+language:
+- en
+license: mit
+base_model: prajjwal1/bert-tiny
+tags:
+- pytorch
+- BertForTokenClassification
+- named-entity-recognition
+- roberta-base
+- generated_from_trainer
+metrics:
+- recall
+- precision
+- f1
+- accuracy
+model-index:
+- name: bert-tiny-ontonotes
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# bert-tiny-ontonotes
+
+This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the tner/ontonotes5 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1917
+- Recall: 0.7193
+- Precision: 0.6817
+- F1: 0.7000
+- Accuracy: 0.9476
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 8e-05
+- train_batch_size: 32
+- eval_batch_size: 160
+- seed: 75241309
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- training_steps: 6000
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Recall | Precision | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
+| 0.4283        | 0.31  | 600  | 0.3864          | 0.4561 | 0.4260    | 0.4405 | 0.9058   |
+| 0.3214        | 0.63  | 1200 | 0.2865          | 0.5865 | 0.5485    | 0.5669 | 0.9265   |
+| 0.2886        | 0.94  | 1800 | 0.2439          | 0.6432 | 0.6165    | 0.6295 | 0.9354   |
+| 0.2511        | 1.25  | 2400 | 0.2233          | 0.6765 | 0.6250    | 0.6497 | 0.9389   |
+| 0.2224        | 1.56  | 3000 | 0.2088          | 0.6878 | 0.6642    | 0.6758 | 0.9433   |
+| 0.2181        | 1.88  | 3600 | 0.2001          | 0.7105 | 0.6684    | 0.6888 | 0.9451   |
+| 0.215         | 2.19  | 4200 | 0.1954          | 0.7140 | 0.6795    | 0.6963 | 0.9469   |
+| 0.1907        | 2.5   | 4800 | 0.1934          | 0.7169 | 0.6776    | 0.6967 | 0.9470   |
+| 0.209         | 2.82  | 5400 | 0.1918          | 0.7185 | 0.6812    | 0.6994 | 0.9475   |
+| 0.2073        | 3.13  | 6000 | 0.1917          | 0.7193 | 0.6817    | 0.7000 | 0.9476   |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.15.0
+- Tokenizers 0.15.0

config.json

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+{
+  "_name_or_path": "prajjwal1/bert-tiny",
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 128,
+  "id2label": {
+    "0": "O",
+    "1": "B-CARDINAL",
+    "2": "B-DATE",
+    "3": "I-DATE",
+    "4": "B-PERSON",
+    "5": "I-PERSON",
+    "6": "B-NORP",
+    "7": "B-GPE",
+    "8": "I-GPE",
+    "9": "B-LAW",
+    "10": "I-LAW",
+    "11": "B-ORG",
+    "12": "I-ORG",
+    "13": "B-PERCENT",
+    "14": "I-PERCENT",
+    "15": "B-ORDINAL",
+    "16": "B-MONEY",
+    "17": "I-MONEY",
+    "18": "B-WORK_OF_ART",
+    "19": "I-WORK_OF_ART",
+    "20": "B-FAC",
+    "21": "B-TIME",
+    "22": "I-CARDINAL",
+    "23": "B-LOC",
+    "24": "B-QUANTITY",
+    "25": "I-QUANTITY",
+    "26": "I-NORP",
+    "27": "I-LOC",
+    "28": "B-PRODUCT",
+    "29": "I-TIME",
+    "30": "B-EVENT",
+    "31": "I-EVENT",
+    "32": "I-FAC",
+    "33": "B-LANGUAGE",
+    "34": "I-PRODUCT",
+    "35": "I-ORDINAL",
+    "36": "I-LANGUAGE"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 512,
+  "label2id": {
+    "B-CARDINAL": 1,
+    "B-DATE": 2,
+    "B-EVENT": 30,
+    "B-FAC": 20,
+    "B-GPE": 7,
+    "B-LANGUAGE": 33,
+    "B-LAW": 9,
+    "B-LOC": 23,
+    "B-MONEY": 16,
+    "B-NORP": 6,
+    "B-ORDINAL": 15,
+    "B-ORG": 11,
+    "B-PERCENT": 13,
+    "B-PERSON": 4,
+    "B-PRODUCT": 28,
+    "B-QUANTITY": 24,
+    "B-TIME": 21,
+    "B-WORK_OF_ART": 18,
+    "I-CARDINAL": 22,
+    "I-DATE": 3,
+    "I-EVENT": 31,
+    "I-FAC": 32,
+    "I-GPE": 8,
+    "I-LANGUAGE": 36,
+    "I-LAW": 10,
+    "I-LOC": 27,
+    "I-MONEY": 17,
+    "I-NORP": 26,
+    "I-ORDINAL": 35,
+    "I-ORG": 12,
+    "I-PERCENT": 14,
+    "I-PERSON": 5,
+    "I-PRODUCT": 34,
+    "I-QUANTITY": 25,
+    "I-TIME": 29,
+    "I-WORK_OF_ART": 19,
+    "O": 0
+  },
+  "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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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runs/Dec20_03-02-36_bb56e48de815/events.out.tfevents.1703041365.bb56e48de815.654.0

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special_tokens_map.json

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

tokenizer_config.json

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+  "add_prefix_space": true,
+  "added_tokens_decoder": {
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+      "lstrip": false,
+      "normalized": false,
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
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+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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