End of training

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-multilingual-cased
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: hindi-distilbert-ner
+  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. -->
+
+# hindi-distilbert-ner
+
+This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0004
+- Precision: 0.9969
+- Recall: 0.9983
+- F1: 0.9976
+- Accuracy: 0.9998
+
+## 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: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step   | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0064        | 1.0   | 47891  | 0.0035          | 0.9860    | 0.9882 | 0.9871 | 0.9990   |
+| 0.0028        | 2.0   | 95782  | 0.0008          | 0.9943    | 0.9978 | 0.9961 | 0.9996   |
+| 0.0006        | 3.0   | 143673 | 0.0004          | 0.9969    | 0.9983 | 0.9976 | 0.9998   |
+
+
+### Framework versions
+
+- Transformers 4.33.0
+- Pytorch 2.0.0
+- Datasets 2.14.5
+- Tokenizers 0.13.3

config.json

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+{
+  "_name_or_path": "distilbert-base-multilingual-cased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForTokenClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "LOCATION",
+    "1": "BRAND",
+    "2": "TITLE_OBJECT",
+    "3": "TITLE_PERSON",
+    "4": "PERSON",
+    "5": "DESIGNATION",
+    "6": "ORGANIZATION",
+    "7": "ABBREVIATION",
+    "8": "TIME",
+    "9": "NUMBER",
+    "10": "MEASURE",
+    "11": "TERMS",
+    "12": "O"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "ABBREVIATION": 7,
+    "BRAND": 1,
+    "DESIGNATION": 5,
+    "LOCATION": 0,
+    "MEASURE": 10,
+    "NUMBER": 9,
+    "O": 12,
+    "ORGANIZATION": 6,
+    "PERSON": 4,
+    "TERMS": 11,
+    "TIME": 8,
+    "TITLE_OBJECT": 2,
+    "TITLE_PERSON": 3
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.0",
+  "vocab_size": 119547
+}

pytorch_model.bin

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+size 539011109

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_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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