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Finished training.

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README.md ADDED
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+ ---
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+ license: mit
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+ library_name: peft
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+ tags:
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+ - parquet
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+ - text-classification
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+ datasets:
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+ - tweet_eval
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+ metrics:
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+ - accuracy
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+ base_model: roberta-base
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+ model-index:
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+ - name: roberta-base-finetuned-lora-tweet_eval_irony
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: tweet_eval
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+ type: tweet_eval
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+ config: irony
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+ split: validation
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+ args: irony
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+ metrics:
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+ - type: accuracy
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+ value: 0.7287958115183246
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+ name: accuracy
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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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+ # roberta-base-finetuned-lora-tweet_eval_irony
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+
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+ This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the tweet_eval dataset.
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+ It achieves the following results on the evaluation set:
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+ - accuracy: 0.7288
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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: 0.0005
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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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: 8
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+
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+ ### Training results
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+
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+ | accuracy | train_loss | epoch |
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+ |:--------:|:----------:|:-----:|
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+ | 0.4775 | None | 0 |
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+ | 0.5675 | 0.6932 | 0 |
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+ | 0.6775 | 0.6398 | 1 |
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+ | 0.7131 | 0.5770 | 2 |
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+ | 0.7361 | 0.5368 | 3 |
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+ | 0.7162 | 0.5292 | 4 |
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+ | 0.7183 | 0.4930 | 5 |
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+ | 0.7340 | 0.4859 | 6 |
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+ | 0.7288 | 0.4808 | 7 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.8.2
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+ - Transformers 4.37.2
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+ - Pytorch 2.2.0
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.2
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+ "init_lora_weights": true,
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+ "layers_pattern": null,
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+ "loftq_config": {},
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+ "lora_dropout": 0.1,
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+ "megatron_core": "megatron.core",
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+ "peft_type": "LORA",
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+ "target_modules": [
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+ "value",
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+ "use_rslora": false
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