Training in progress, step 24
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README.md
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---
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library_name: transformers
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license: mit
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base_model: roberta-base
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tags:
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- generated_from_trainer
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model-index:
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- name: basil_topic_subsamples_model_alpha_1000_idx_1
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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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# basil_topic_subsamples_model_alpha_1000_idx_1
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
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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: 64
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- eval_batch_size: 64
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- seed: 42
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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: linear
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- num_epochs: 3
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### Training results
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### Framework versions
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- Transformers 4.47.0
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- Pytorch 2.5.1+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": "roberta-base",
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"architectures": [
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"RobertaForSequenceClassification"
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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": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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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": 50265
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}
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special_tokens_map.json
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tokenizer.json
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tokenizer_config.json
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training_args.bin
ADDED
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vocab.json
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