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Training complete

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README.md ADDED
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+ ---
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+ base_model: dccuchile/albert-base-spanish
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+ tags:
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+ - classification
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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: clasificador-muchocine-modeloalbert
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+ results: []
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+ ---
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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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+
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+ # clasificador-muchocine-modeloalbert
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+
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+ This model is a fine-tuned version of [dccuchile/albert-base-spanish](https://huggingface.co/dccuchile/albert-base-spanish) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.3035
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+ - Accuracy: 0.4465
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-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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+ - num_epochs: 3.0
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 388 | 1.3477 | 0.3935 |
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+ | 1.366 | 2.0 | 776 | 1.2426 | 0.4361 |
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+ | 0.9842 | 3.0 | 1164 | 1.3035 | 0.4465 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.0
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+ "_name_or_path": "dccuchile/albert-base-spanish",
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+ "architectures": [
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+ "AlbertForSequenceClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0,
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+ "gap_size": 0,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0,
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "albert",
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+ "net_structure_type": 0,
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+ "num_attention_heads": 12,
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+ "problem_type": "single_label_classification",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.35.2",
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+ "type_vocab_size": 2,
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+ "vocab_size": 31000
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+ }
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