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README.md ADDED
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+ ---
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+ license: mit
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+ base_model: xlm-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: xlmr-finetuned
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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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+ # xlmr-finetuned
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+
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+ This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.3897
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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: 16
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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: 10
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:-----:|:-----:|:---------------:|
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+ | 3.1718 | 0.29 | 500 | 2.5733 |
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+ | 2.8822 | 0.59 | 1000 | 2.3739 |
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+ | 2.7361 | 0.88 | 1500 | 2.3563 |
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+ | 2.6077 | 1.18 | 2000 | 2.2466 |
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+ | 2.4731 | 1.47 | 2500 | 2.2027 |
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+ | 2.4545 | 1.76 | 3000 | 2.2104 |
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+ | 2.467 | 2.06 | 3500 | 2.0885 |
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+ | 2.3209 | 2.35 | 4000 | 2.0476 |
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+ | 2.2937 | 2.64 | 4500 | 1.9431 |
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+ | 2.2624 | 2.94 | 5000 | 1.9157 |
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+ | 2.1502 | 3.23 | 5500 | 1.8811 |
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+ | 2.1445 | 3.53 | 6000 | 1.8488 |
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+ | 2.1308 | 3.82 | 6500 | 1.8074 |
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+ | 2.0752 | 4.11 | 7000 | 1.8089 |
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+ | 2.032 | 4.41 | 7500 | 1.7853 |
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+ | 2.0253 | 4.7 | 8000 | 1.7723 |
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+ | 1.9904 | 4.99 | 8500 | 1.6976 |
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+ | 1.9348 | 5.29 | 9000 | 1.6399 |
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+ | 1.9116 | 5.58 | 9500 | 1.6159 |
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+ | 1.9105 | 5.88 | 10000 | 1.5930 |
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+ | 1.8649 | 6.17 | 10500 | 1.5590 |
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+ | 1.8108 | 6.46 | 11000 | 1.5662 |
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+ | 1.8084 | 6.76 | 11500 | 1.5504 |
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+ | 1.7835 | 7.05 | 12000 | 1.5933 |
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+ | 1.7324 | 7.34 | 12500 | 1.5500 |
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+ | 1.7358 | 7.64 | 13000 | 1.4570 |
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+ | 1.726 | 7.93 | 13500 | 1.4775 |
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+ | 1.6477 | 8.23 | 14000 | 1.4382 |
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+ | 1.6768 | 8.52 | 14500 | 1.4717 |
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+ | 1.6073 | 8.81 | 15000 | 1.4162 |
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+ | 1.6516 | 9.11 | 15500 | 1.4516 |
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+ | 1.6084 | 9.4 | 16000 | 1.4209 |
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+ | 1.6013 | 9.69 | 16500 | 1.3874 |
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+ | 1.608 | 9.99 | 17000 | 1.3897 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.36.2
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+ - Pytorch 2.0.0
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.0
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