End of training
Browse files- .gitattributes +1 -0
- README.md +79 -0
- config.json +67 -0
- model.safetensors +3 -0
- special_tokens_map.json +15 -0
- tokenizer.json +3 -0
- tokenizer_config.json +55 -0
- training_args.bin +3 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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library_name: transformers
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language:
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- uz
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license: mit
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base_model: FacebookAI/xlm-roberta-large
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tags:
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- generated_from_trainer
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datasets:
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- risqaliyevds/uzbek_ner
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: Uzbek NER model
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Uzbek NER model
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the Uzbek Ner dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1421
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- Precision: 0.6071
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- Recall: 0.6482
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- F1: 0.6270
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- Accuracy: 0.9486
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 64
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 3
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.1671 | 0.5758 | 150 | 0.1632 | 0.5260 | 0.6425 | 0.5785 | 0.9402 |
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| 0.1453 | 1.1497 | 300 | 0.1481 | 0.5935 | 0.6191 | 0.6061 | 0.9467 |
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| 0.134 | 1.7255 | 450 | 0.1449 | 0.5936 | 0.6216 | 0.6073 | 0.9480 |
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| 0.1273 | 2.2994 | 600 | 0.1413 | 0.6217 | 0.6262 | 0.6239 | 0.9493 |
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| 0.1258 | 2.8752 | 750 | 0.1421 | 0.6071 | 0.6482 | 0.6270 | 0.9486 |
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### Framework versions
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- Transformers 4.47.0
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- Pytorch 2.1.1+cu121
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- Datasets 3.2.0
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- Tokenizers 0.21.0
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config.json
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{
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"_name_or_path": "FacebookAI/xlm-roberta-large",
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"architectures": [
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"XLMRobertaForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"classifier_dropout": null,
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"eos_token_id": 2,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"id2label": {
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"0": "LABEL_0",
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"1": "LABEL_1",
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"2": "LABEL_2",
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"3": "LABEL_3",
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"4": "LABEL_4",
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"5": "LABEL_5",
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"6": "LABEL_6",
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"7": "LABEL_7",
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"8": "LABEL_8",
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"9": "LABEL_9",
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"10": "LABEL_10"
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},
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"label2id": {
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},
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"layer_norm_eps": 1e-05,
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"loss_weight": [
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"max_position_embeddings": 514,
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"model_type": "xlm-roberta",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"output_past": true,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.47.0",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 2235456956
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special_tokens_map.json
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{
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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size 17082999
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tokenizer_config.json
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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size 5240
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