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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-large-xlsr-53 |
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tags: |
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- automatic-speech-recognition |
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- wav_sub-P001 |
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- generated_from_trainer |
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datasets: |
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- audiofolder |
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metrics: |
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- wer |
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model-index: |
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- name: em_ctc |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: WAV_SUB-P001 - TR |
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type: audiofolder |
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config: default |
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split: validation |
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args: 'Config: tr, Training split: train+validation, Eval split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.9843253066787824 |
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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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# em_ctc |
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This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the WAV_SUB-P001 - TR dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.7286 |
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- Wer: 0.9843 |
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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: 0.0003 |
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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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 20.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| No log | 0.16 | 100 | 3.8990 | 1.0 | |
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| No log | 0.32 | 200 | 2.8678 | 1.0 | |
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| No log | 0.48 | 300 | 2.7795 | 1.0 | |
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| No log | 0.65 | 400 | 2.7316 | 1.0 | |
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| 4.389 | 0.81 | 500 | 2.6889 | 0.9784 | |
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| 4.389 | 0.97 | 600 | 3.2727 | 0.9784 | |
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| 4.389 | 1.13 | 700 | 2.7057 | 0.9784 | |
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| 4.389 | 1.29 | 800 | 2.8525 | 0.9964 | |
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| 4.389 | 1.45 | 900 | 2.6685 | 0.9968 | |
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| 2.5649 | 1.61 | 1000 | 2.7403 | 1.0 | |
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| 2.5649 | 1.78 | 1100 | 2.7790 | 1.0 | |
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| 2.5649 | 1.94 | 1200 | 2.8130 | 0.9786 | |
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| 2.5649 | 2.1 | 1300 | 2.8031 | 1.0 | |
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| 2.5649 | 2.26 | 1400 | 2.9683 | 1.0 | |
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| 2.5421 | 2.42 | 1500 | 2.9459 | 0.9784 | |
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| 2.5421 | 2.58 | 1600 | 2.7052 | 0.9784 | |
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| 2.5421 | 2.74 | 1700 | 2.7879 | 0.9786 | |
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| 2.5421 | 2.91 | 1800 | 2.7956 | 1.0 | |
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| 2.5421 | 3.07 | 1900 | 2.7760 | 0.9784 | |
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| 2.5357 | 3.23 | 2000 | 2.8594 | 0.9995 | |
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| 2.5357 | 3.39 | 2100 | 2.9048 | 0.9796 | |
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| 2.5357 | 3.55 | 2200 | 3.0098 | 0.9784 | |
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| 2.5357 | 3.71 | 2300 | 2.7079 | 0.9784 | |
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| 2.5357 | 3.87 | 2400 | 3.2403 | 1.0 | |
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| 2.5203 | 4.04 | 2500 | 3.0476 | 0.9784 | |
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| 2.5203 | 4.2 | 2600 | 2.8510 | 1.0 | |
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| 2.5203 | 4.36 | 2700 | 2.7907 | 0.9784 | |
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| 2.5203 | 4.52 | 2800 | 2.7486 | 0.9784 | |
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| 2.5203 | 4.68 | 2900 | 3.1701 | 1.0 | |
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| 2.5191 | 4.84 | 3000 | 2.9529 | 0.9784 | |
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| 2.5191 | 5.0 | 3100 | 3.1192 | 0.9650 | |
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| 2.5191 | 5.17 | 3200 | 2.8596 | 1.0 | |
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| 2.5191 | 5.33 | 3300 | 2.9193 | 1.0 | |
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| 2.5191 | 5.49 | 3400 | 3.0367 | 0.9784 | |
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| 2.5422 | 5.65 | 3500 | 2.9162 | 0.9784 | |
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| 2.5422 | 5.81 | 3600 | 3.0334 | 1.0 | |
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| 2.5422 | 5.97 | 3700 | 2.8514 | 0.9784 | |
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| 2.5422 | 6.13 | 3800 | 2.9654 | 1.0 | |
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| 2.5422 | 6.3 | 3900 | 3.2616 | 0.9784 | |
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| 2.5062 | 6.46 | 4000 | 3.3320 | 0.9793 | |
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| 2.5062 | 6.62 | 4100 | 2.7141 | 0.9784 | |
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| 2.5062 | 6.78 | 4200 | 3.2108 | 0.9784 | |
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| 2.5062 | 6.94 | 4300 | 3.0015 | 0.9784 | |
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| 2.5062 | 7.1 | 4400 | 3.0244 | 1.0 | |
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| 2.5114 | 7.26 | 4500 | 2.8742 | 0.9784 | |
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| 2.5114 | 7.43 | 4600 | 3.1471 | 0.9784 | |
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| 2.5114 | 7.59 | 4700 | 2.7006 | 0.9773 | |
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| 2.5114 | 7.75 | 4800 | 3.1189 | 1.0 | |
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| 2.5114 | 7.91 | 4900 | 3.1604 | 0.9784 | |
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| 2.5065 | 8.07 | 5000 | 2.9297 | 0.9784 | |
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| 2.5065 | 8.23 | 5100 | 3.0998 | 0.9784 | |
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| 2.5065 | 8.39 | 5200 | 2.8184 | 0.9843 | |
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| 2.5065 | 8.56 | 5300 | 2.7133 | 0.9861 | |
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| 2.5065 | 8.72 | 5400 | 2.7399 | 0.9811 | |
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| 2.4956 | 8.88 | 5500 | 2.7186 | 0.9889 | |
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| 2.4956 | 9.04 | 5600 | 2.9872 | 0.9955 | |
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| 2.4956 | 9.2 | 5700 | 3.0825 | 0.9993 | |
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| 2.4956 | 9.36 | 5800 | 3.0589 | 0.9855 | |
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| 2.4956 | 9.52 | 5900 | 2.8177 | 0.9784 | |
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| 2.4774 | 9.69 | 6000 | 2.8104 | 0.9993 | |
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| 2.4774 | 9.85 | 6100 | 2.9498 | 0.9796 | |
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| 2.4774 | 10.01 | 6200 | 3.0006 | 0.9784 | |
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| 2.4774 | 10.17 | 6300 | 2.8100 | 0.9784 | |
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| 2.4774 | 10.33 | 6400 | 3.1577 | 0.9786 | |
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| 2.4689 | 10.49 | 6500 | 2.7814 | 0.9977 | |
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| 2.4689 | 10.65 | 6600 | 2.7271 | 0.9836 | |
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| 2.4689 | 10.82 | 6700 | 2.8403 | 0.9784 | |
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| 2.4689 | 10.98 | 6800 | 2.7257 | 0.9998 | |
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| 2.4689 | 11.14 | 6900 | 2.6728 | 0.9898 | |
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| 2.486 | 11.3 | 7000 | 2.7348 | 0.9809 | |
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| 2.486 | 11.46 | 7100 | 2.7054 | 0.9982 | |
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| 2.486 | 11.62 | 7200 | 2.7254 | 0.9948 | |
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| 2.486 | 11.78 | 7300 | 2.7498 | 0.9891 | |
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| 2.486 | 11.95 | 7400 | 2.7076 | 0.9898 | |
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| 2.4616 | 12.11 | 7500 | 2.6398 | 0.9995 | |
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| 2.4616 | 12.27 | 7600 | 2.7626 | 0.9846 | |
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| 2.4616 | 12.43 | 7700 | 2.6804 | 0.9814 | |
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| 2.4616 | 12.59 | 7800 | 2.8212 | 0.9834 | |
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| 2.4616 | 12.75 | 7900 | 2.6535 | 0.9959 | |
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| 2.4573 | 12.91 | 8000 | 2.7547 | 0.9993 | |
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| 2.4573 | 13.08 | 8100 | 2.7253 | 0.9798 | |
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| 2.4573 | 13.24 | 8200 | 2.6851 | 0.9936 | |
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| 2.4573 | 13.4 | 8300 | 2.7627 | 0.9907 | |
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| 2.4573 | 13.56 | 8400 | 2.6607 | 0.9857 | |
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| 2.4487 | 13.72 | 8500 | 2.6645 | 0.9800 | |
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| 2.4487 | 13.88 | 8600 | 2.7558 | 0.9973 | |
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| 2.4487 | 14.04 | 8700 | 2.7665 | 0.9961 | |
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| 2.4487 | 14.21 | 8800 | 2.7697 | 0.9827 | |
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| 2.4487 | 14.37 | 8900 | 2.8531 | 0.9918 | |
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| 2.4416 | 14.53 | 9000 | 2.8974 | 0.9920 | |
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| 2.4416 | 14.69 | 9100 | 2.7308 | 0.9975 | |
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| 2.4416 | 14.85 | 9200 | 2.7919 | 0.9816 | |
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| 2.4416 | 15.01 | 9300 | 2.6605 | 0.9893 | |
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| 2.4416 | 15.17 | 9400 | 2.6058 | 0.9816 | |
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| 2.4405 | 15.33 | 9500 | 2.6366 | 0.9911 | |
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| 2.4405 | 15.5 | 9600 | 2.5653 | 0.9818 | |
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| 2.4405 | 15.66 | 9700 | 2.7026 | 0.9807 | |
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| 2.4405 | 15.82 | 9800 | 2.7358 | 0.9796 | |
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| 2.4405 | 15.98 | 9900 | 2.6954 | 0.9848 | |
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| 2.4352 | 16.14 | 10000 | 2.6610 | 0.9857 | |
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| 2.4352 | 16.3 | 10100 | 2.7686 | 0.9811 | |
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| 2.4352 | 16.46 | 10200 | 2.7758 | 0.9798 | |
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| 2.4352 | 16.63 | 10300 | 2.7515 | 0.9848 | |
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| 2.4352 | 16.79 | 10400 | 2.7264 | 0.9911 | |
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| 2.4354 | 16.95 | 10500 | 2.7039 | 0.9791 | |
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| 2.4354 | 17.11 | 10600 | 2.7580 | 0.9843 | |
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| 2.4354 | 17.27 | 10700 | 2.7187 | 0.9855 | |
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| 2.4354 | 17.43 | 10800 | 2.7545 | 0.9798 | |
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| 2.4354 | 17.59 | 10900 | 2.7452 | 0.9809 | |
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| 2.4321 | 17.76 | 11000 | 2.6804 | 0.9836 | |
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| 2.4321 | 17.92 | 11100 | 2.6586 | 0.9891 | |
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| 2.4321 | 18.08 | 11200 | 2.6805 | 0.9830 | |
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| 2.4321 | 18.24 | 11300 | 2.6626 | 0.9871 | |
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| 2.4321 | 18.4 | 11400 | 2.7002 | 0.9809 | |
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| 2.4193 | 18.56 | 11500 | 2.7054 | 0.9839 | |
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| 2.4193 | 18.72 | 11600 | 2.7171 | 0.9900 | |
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| 2.4193 | 18.89 | 11700 | 2.7122 | 0.9852 | |
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| 2.4193 | 19.05 | 11800 | 2.7058 | 0.9871 | |
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| 2.4193 | 19.21 | 11900 | 2.7004 | 0.9839 | |
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| 2.4276 | 19.37 | 12000 | 2.7250 | 0.9852 | |
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| 2.4276 | 19.53 | 12100 | 2.7126 | 0.9861 | |
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| 2.4276 | 19.69 | 12200 | 2.7388 | 0.9834 | |
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| 2.4276 | 19.85 | 12300 | 2.7311 | 0.9850 | |
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### Framework versions |
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- Transformers 4.34.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.0 |
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