End of training

README.md

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+---
+license: apache-2.0
+base_model: google/muril-base-cased
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: hindi-muril-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-muril-ner
+
+This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0006
+- Precision: 0.9963
+- Recall: 0.9984
+- F1: 0.9973
+- Accuracy: 0.9997
+
+## 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 |
+|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0065        | 1.0   | 47891  | 0.0044          | 0.9866    | 0.9900 | 0.9883 | 0.9988   |
+| 0.0016        | 2.0   | 95782  | 0.0011          | 0.9952    | 0.9975 | 0.9963 | 0.9996   |
+| 0.0008        | 3.0   | 143673 | 0.0006          | 0.9963    | 0.9984 | 0.9973 | 0.9997   |
+
+
+### 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": "google/muril-base-cased",
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "embedding_size": 768,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LOCATION",
+    "1": "BRAND",
+    "2": "TITLE_OBJECT",
+    "3": "PERSON",
+    "4": "DESIGNATION",
+    "5": "ORGANIZATION",
+    "6": "ABBREVIATION",
+    "7": "TIME",
+    "8": "NUMBER",
+    "9": "MEASURE",
+    "10": "TERMS",
+    "11": "O"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "ABBREVIATION": 6,
+    "BRAND": 1,
+    "DESIGNATION": 4,
+    "LOCATION": 0,
+    "MEASURE": 9,
+    "NUMBER": 8,
+    "O": 11,
+    "ORGANIZATION": 5,
+    "PERSON": 3,
+    "TERMS": 10,
+    "TIME": 7,
+    "TITLE_OBJECT": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 197285
+}

pytorch_model.bin

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+size 947967145

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

training_args.bin

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