Model save

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+library_name: transformers
+license: mit
+base_model: xlm-roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: lang-ner-xlmr
+  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. -->
+
+# lang-ner-xlmr
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0404
+- Precision: 0.8848
+- Recall: 0.9012
+- F1: 0.8929
+- Accuracy: 0.9909
+
+## 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: 5e-05
+- train_batch_size: 72
+- eval_batch_size: 36
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 144
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 2
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0465        | 0.1447 | 2500  | 0.0819          | 0.7945    | 0.8602 | 0.8260 | 0.9828   |
+| 0.0440        | 0.2894 | 5000  | 0.0703          | 0.8023    | 0.8662 | 0.8330 | 0.9843   |
+| 0.0351        | 0.4342 | 7500  | 0.0611          | 0.8427    | 0.8800 | 0.8609 | 0.9860   |
+| 0.0314        | 0.5789 | 10000 | 0.0593          | 0.8542    | 0.8851 | 0.8694 | 0.9872   |
+| 0.0329        | 0.7236 | 12500 | 0.0563          | 0.8394    | 0.8781 | 0.8583 | 0.9868   |
+| 0.0281        | 0.8683 | 15000 | 0.0488          | 0.8595    | 0.8853 | 0.8722 | 0.9886   |
+| 0.0274        | 1.0130 | 17500 | 0.0477          | 0.8623    | 0.8904 | 0.8761 | 0.9894   |
+| 0.0236        | 1.1577 | 20000 | 0.0483          | 0.8675    | 0.8933 | 0.8802 | 0.9894   |
+| 0.0235        | 1.3025 | 22500 | 0.0461          | 0.8720    | 0.8933 | 0.8825 | 0.9901   |
+| 0.0195        | 1.4472 | 25000 | 0.0439          | 0.8755    | 0.8954 | 0.8853 | 0.9903   |
+| 0.0222        | 1.5919 | 27500 | 0.0442          | 0.8765    | 0.8964 | 0.8863 | 0.9901   |
+| 0.0194        | 1.7366 | 30000 | 0.0438          | 0.8803    | 0.8993 | 0.8897 | 0.9902   |
+| 0.0200        | 1.8814 | 32500 | 0.0404          | 0.8848    | 0.9012 | 0.8929 | 0.9909   |
+
+
+### Framework versions
+
+- Transformers 5.0.0
+- Pytorch 2.10.0+cu128
+- Datasets 4.0.0
+- Tokenizers 0.22.2

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tokenizer_config.json

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+  "add_prefix_space": true,
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "is_local": false,
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}