mOvie/roberta-base-klue-ynat-classification

README.md

@@ -3,8 +3,6 @@ library_name: transformers
 base_model: klue/roberta-base
 tags:
 - generated_from_trainer
-metrics:
-- accuracy
 model-index:
 - name: roberta-base-klue-ynat-classification
   results: []
@@ -16,9 +14,6 @@ should probably proofread and complete it, then remove this comment. -->
 # roberta-base-klue-ynat-classification
 
 This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.4612
-- Accuracy: 0.859
 
 ## Model description
 
@@ -41,7 +36,7 @@ The following hyperparameters were used during training:
 - train_batch_size: 8
 - eval_batch_size: 8
 - seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- 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: 1
 
@@ -51,7 +46,7 @@ The following hyperparameters were used during training:
 
 ### Framework versions
 
-- Transformers 4.54.0
-- Pytorch 2.6.0+cu124
+- Transformers 4.57.2
+- Pytorch 2.9.0+cu126
 - Datasets 4.0.0
-- Tokenizers 0.21.2
+- Tokenizers 0.22.1

config.json

@@ -5,6 +5,7 @@
   "attention_probs_dropout_prob": 0.1,
   "bos_token_id": 0,
   "classifier_dropout": null,
+  "dtype": "float32",
   "eos_token_id": 2,
   "gradient_checkpointing": false,
   "hidden_act": "gelu",
@@ -39,8 +40,7 @@
   "position_embedding_type": "absolute",
   "problem_type": "single_label_classification",
   "tokenizer_class": "BertTokenizer",
-  "torch_dtype": "float32",
-  "transformers_version": "4.54.0",
+  "transformers_version": "4.57.2",
   "type_vocab_size": 1,
   "use_cache": true,
   "vocab_size": 32000

model.safetensors

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training_args.bin

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