Instructions to use a2ran/FingerFriend-t5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use a2ran/FingerFriend-t5-small with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("a2ran/FingerFriend-t5-small") model = AutoModelForSeq2SeqLM.from_pretrained("a2ran/FingerFriend-t5-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +11 -16
- config.json +11 -39
- generation_config.json +1 -0
- pytorch_model.bin +2 -2
- special_tokens_map.json +1 -1
- tokenizer.json +0 -0
- tokenizer_config.json +6 -2
- training_args.bin +1 -1
README.md
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---
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base_model: t5-small
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tags:
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- generated_from_trainer
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model-index:
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# FingerFriend-t5-small
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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## Model description
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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:
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- eval_batch_size:
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- seed: 42
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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 Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 0.921 | 7.0 | 1064 | 0.8384 |
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| 0.9018 | 8.0 | 1216 | 0.8176 |
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| 0.8801 | 9.0 | 1368 | 0.8030 |
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| 0.8567 | 10.0 | 1520 | 0.7932 |
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### Framework versions
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---
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base_model: eenzeenee/t5-small-korean-summarization
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tags:
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- generated_from_trainer
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model-index:
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# FingerFriend-t5-small
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This model is a fine-tuned version of [eenzeenee/t5-small-korean-summarization](https://huggingface.co/eenzeenee/t5-small-korean-summarization) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6002
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## Model description
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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: 8
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- eval_batch_size: 8
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- seed: 42
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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 Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 1.4509 | 1.0 | 342 | 0.7916 |
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| 0.8917 | 2.0 | 684 | 0.7062 |
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| 0.7846 | 3.0 | 1026 | 0.6640 |
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| 0.72 | 4.0 | 1368 | 0.6334 |
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| 0.6804 | 5.0 | 1710 | 0.6100 |
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| 0.6387 | 6.0 | 2052 | 0.6002 |
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### Framework versions
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config.json
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{
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"_name_or_path": "t5-small",
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"architectures": [
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"T5ForConditionalGeneration"
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],
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"classifier_dropout": 0.0,
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-
"d_ff":
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"d_kv": 64,
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"d_model": 512,
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"decoder_start_token_id": 0,
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"dense_act_fn": "
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"dropout_rate": 0.1,
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"eos_token_id": 1,
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"feed_forward_proj": "
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"initializer_factor": 1.0,
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"is_encoder_decoder": true,
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"is_gated_act":
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"layer_norm_epsilon": 1e-06,
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"model_type": "t5",
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"
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"
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"
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"num_layers": 6,
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"output_past": true,
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"pad_token_id": 0,
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"relative_attention_max_distance": 128,
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"relative_attention_num_buckets": 32,
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"
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"summarization": {
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"early_stopping": true,
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"length_penalty": 2.0,
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"max_length": 200,
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"min_length": 30,
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"no_repeat_ngram_size": 3,
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"num_beams": 4,
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"prefix": "summarize: "
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},
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"translation_en_to_de": {
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"early_stopping": true,
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"max_length": 300,
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"num_beams": 4,
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"prefix": "translate English to German: "
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},
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"translation_en_to_fr": {
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"early_stopping": true,
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"max_length": 300,
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"num_beams": 4,
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"prefix": "translate English to French: "
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},
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"translation_en_to_ro": {
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"early_stopping": true,
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"max_length": 300,
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"num_beams": 4,
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"prefix": "translate English to Romanian: "
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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.33.2",
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"use_cache": true,
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"vocab_size":
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}
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{
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"_name_or_path": "eenzeenee/t5-small-korean-summarization",
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"architectures": [
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"T5ForConditionalGeneration"
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],
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"classifier_dropout": 0.0,
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"d_ff": 1024,
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"d_kv": 64,
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"d_model": 512,
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"decoder_start_token_id": 0,
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"dense_act_fn": "gelu_new",
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"dropout_rate": 0.1,
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"eos_token_id": 1,
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"feed_forward_proj": "gated-gelu",
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"initializer_factor": 1.0,
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"is_encoder_decoder": true,
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"is_gated_act": true,
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"layer_norm_epsilon": 1e-06,
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"max_length": 128,
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"model_type": "t5",
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"num_decoder_layers": 8,
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"num_heads": 6,
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"num_layers": 8,
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"output_past": true,
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"pad_token_id": 0,
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"relative_attention_max_distance": 128,
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"relative_attention_num_buckets": 32,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.33.2",
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"use_cache": true,
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"vocab_size": 50358
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}
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generation_config.json
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"_from_model_config": true,
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"decoder_start_token_id": 0,
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"eos_token_id": 1,
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"pad_token_id": 0,
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"transformers_version": "4.33.2"
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}
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"_from_model_config": true,
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"decoder_start_token_id": 0,
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"eos_token_id": 1,
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"max_length": 128,
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"pad_token_id": 0,
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"transformers_version": "4.33.2"
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}
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pytorch_model.bin
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special_tokens_map.json
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],
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"eos_token": "</s>",
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"pad_token": "<pad>",
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}
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"eos_token": "</s>",
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"pad_token": "<pad>",
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"unk_token": "<pad>"
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}
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tokenizer.json
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tokenizer_config.json
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"clean_up_tokenization_spaces": true,
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"eos_token": "</s>",
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"extra_ids": 100,
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"
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"pad_token": "<pad>",
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"tokenizer_class": "T5Tokenizer",
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"
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}
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"clean_up_tokenization_spaces": true,
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"eos_token": "</s>",
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"extra_ids": 100,
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"max_length": 128,
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"model_max_length": 128,
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"pad_token": "<pad>",
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"stride": 128,
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"tokenizer_class": "T5Tokenizer",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "<pad>"
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}
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size 4155
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