Uploading checkpoint-98500 for xlm-r - dan-latn
Browse files- .gitattributes +1 -0
- README.md +67 -0
- config.json +34 -0
- model.safetensors +3 -0
- optimizer.pt +3 -0
- rng_state_0.pth +3 -0
- rng_state_1.pth +3 -0
- scheduler.pt +3 -0
- sentencepiece.bpe.model +3 -0
- special_tokens_map.json +15 -0
- tokenizer.json +3 -0
- tokenizer_config.json +54 -0
- trainer_state.json +3185 -0
- training_args.bin +3 -0
.gitattributes
CHANGED
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@@ -33,3 +33,4 @@ saved_model/**/* 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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*.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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@@ -0,0 +1,67 @@
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---
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library_name: transformers
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license: mit
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base_model: FacebookAI/xlm-roberta-base
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: dan-Latn
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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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# dan-Latn
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: nan
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- Accuracy: 0.8001
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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: 0.0001
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 2
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- total_train_batch_size: 32
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- total_eval_batch_size: 32
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- training_steps: 100000
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### Citation Information
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If you use this model in your work, please cite the following paper. Additionally, if you require more details on training and performance, refer to the paper:
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@misc{gurgurov2025smallmodelsbigimpact,
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title={Small Models, Big Impact: Efficient Corpus and Graph-Based Adaptation of Small Multilingual Language Models for Low-Resource Languages},
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author={Daniil Gurgurov and Ivan Vykopal and Josef van Genabith and Simon Ostermann},
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year={2025},
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eprint={2502.10140},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2502.10140},
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}
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config.json
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{
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"_name_or_path": "FacebookAI/xlm-roberta-base",
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"adapters": {
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"adapters": {},
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"config_map": {},
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"fusion_config_map": {},
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"fusions": {}
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},
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"architectures": [
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"XLMRobertaForMaskedLM"
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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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| 14 |
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"classifier_dropout": null,
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| 15 |
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"eos_token_id": 2,
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| 16 |
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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| 20 |
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-05,
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| 22 |
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"max_position_embeddings": 514,
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"model_type": "xlm-roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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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": "4.45.2",
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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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oid sha256:a196d0b4f8981b4a78fb32b244ec8830f34fc22c516268812fb734415d4077bf
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size 1113205088
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optimizer.pt
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version https://git-lfs.github.com/spec/v1
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size 2226530490
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rng_state_0.pth
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version https://git-lfs.github.com/spec/v1
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size 14512
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rng_state_1.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:be4bf09758aed1f2eab8363a2457a8619781a1ecd62a548afe0c7735c3fcc5bf
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size 14512
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scheduler.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:9b72c24b8eeac82a0d428f6aaf2686dbda50deecaee6ca497e01bfbb1a32f05c
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size 1064
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sentencepiece.bpe.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
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size 5069051
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special_tokens_map.json
ADDED
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{
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"bos_token": "<s>",
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"cls_token": "<s>",
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| 4 |
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"eos_token": "</s>",
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| 5 |
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"mask_token": {
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| 6 |
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"content": "<mask>",
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| 7 |
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"lstrip": true,
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| 8 |
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"normalized": false,
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| 9 |
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"rstrip": false,
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| 10 |
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"single_word": false
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},
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| 12 |
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"unk_token": "<unk>"
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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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oid sha256:8373f9cd3d27591e1924426bcc1c8799bc5a9affc4fc857982c5d66668dd1f41
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+
size 17082832
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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| 3 |
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"0": {
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| 4 |
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"content": "<s>",
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| 5 |
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"lstrip": false,
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| 6 |
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"normalized": false,
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| 7 |
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"rstrip": false,
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| 8 |
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"single_word": false,
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| 9 |
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"special": true
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| 10 |
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},
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| 11 |
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"1": {
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| 12 |
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"content": "<pad>",
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| 13 |
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"lstrip": false,
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| 14 |
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"normalized": false,
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| 15 |
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"rstrip": false,
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| 16 |
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"single_word": false,
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| 17 |
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"special": true
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| 18 |
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},
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| 19 |
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"2": {
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| 20 |
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"content": "</s>",
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| 21 |
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"lstrip": false,
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| 22 |
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"normalized": false,
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| 23 |
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"rstrip": false,
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| 24 |
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"single_word": false,
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| 25 |
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"special": true
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| 26 |
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},
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| 27 |
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"3": {
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| 28 |
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"content": "<unk>",
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| 29 |
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"lstrip": false,
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| 30 |
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"normalized": false,
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| 31 |
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"rstrip": false,
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| 32 |
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"single_word": false,
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| 33 |
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"special": true
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| 34 |
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},
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| 35 |
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"250001": {
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| 36 |
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"content": "<mask>",
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| 37 |
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"lstrip": true,
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| 38 |
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"normalized": false,
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| 39 |
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"rstrip": false,
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| 40 |
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"single_word": false,
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| 41 |
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"special": true
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| 42 |
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}
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| 43 |
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},
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| 44 |
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"bos_token": "<s>",
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| 45 |
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"clean_up_tokenization_spaces": false,
|
| 46 |
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"cls_token": "<s>",
|
| 47 |
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"eos_token": "</s>",
|
| 48 |
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"mask_token": "<mask>",
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| 49 |
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"model_max_length": 512,
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| 50 |
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"pad_token": "<pad>",
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| 51 |
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"sep_token": "</s>",
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| 52 |
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"tokenizer_class": "XLMRobertaTokenizer",
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| 53 |
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"unk_token": "<unk>"
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| 54 |
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}
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trainer_state.json
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|
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:ea3ad00921e177c34563af8b442bea2f21652ed0254afaeb3ef25cb047cc1c7a
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| 3 |
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size 5176
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