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--- |
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language: |
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- ca |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- collectivat/tv3_parla |
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- generated_from_trainer |
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- hf-asr-leaderboard |
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- mozilla-foundation/common_voice_8_0 |
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- projecte-aina/parlament_parla |
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- robust-speech-event |
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datasets: |
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- mozilla-foundation/common_voice_8_0 |
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- collectivat/tv3_parla |
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- projecte-aina/parlament_parla |
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model-index: |
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- name: wav2vec2-xls-r-300m-ca |
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results: |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: mozilla-foundation/common_voice_8_0 ca |
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type: mozilla-foundation/common_voice_8_0 |
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args: ca |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 13.170091241317552 |
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- name: Test CER |
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type: cer |
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value: 3.356726205534543 |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: projecte-aina/parlament_parla ca |
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type: projecte-aina/parlament_parla |
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args: clean |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 8.048005647723261 |
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- name: Test CER |
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type: cer |
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value: 2.240912911020065 |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: collectivat/tv3_parla ca |
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type: collectivat/tv3_parla |
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args: ca |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 23.320629787889285 |
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- name: Test CER |
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type: cer |
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value: 10.439216202089989 |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: speech-recognition-community-v2/dev_data ca |
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type: speech-recognition-community-v2/dev_data |
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args: ca |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 31.99671115046487 |
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- name: Test CER |
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type: cer |
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value: 15.820020687277325 |
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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: Robust Speech Event - Test Data |
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type: speech-recognition-community-v2/eval_data |
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args: ca |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 22.04 |
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--- |
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# wav2vec2-xls-r-300m-ca |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA, the [tv3_parla](https://huggingface.co/datasets/collectivat/tv3_parla) and [parlament_parla](https://huggingface.co/datasets/projecte-aina/parlament_parla) datasets. |
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It achieves the following results on the evaluation set (for the three datasets): |
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- Loss: 0.2472 |
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- Wer: 0.1499 |
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## Model description |
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Please check the original [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) Model card. This is just a finetuned version of that model. |
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## Intended uses & limitations |
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As any model trained on crowdsourced data, this model can show the biases and particularities of the data and model used to train this model. Moreover, since this is a speech recognition model, it may underperform for some lower-resourced dialects for the catalan language. |
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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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The data is preprocessed to remove characters not on the catalan alphabet. Moreover, numbers are verbalized using code provided by [@ccoreilly](https://github.com/ccoreilly), which can be found on the text/ folder or [here](https://github.com/CollectivaT-dev/catotron-cpu/blob/master/text/numbers_ca.py). |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 7.5e-05 |
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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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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 2000 |
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- num_epochs: 18.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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Check the Tensorboard tab to check the training profile and evaluation results along training. The model was evaluated on the test splits for each of the datasets used during training. |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 6.2099 | 0.09 | 500 | 3.4125 | 1.0 | |
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| 2.9961 | 0.18 | 1000 | 2.9224 | 1.0 | |
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| 2.2147 | 0.26 | 1500 | 0.6521 | 0.5568 | |
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| 1.3017 | 0.35 | 2000 | 0.3153 | 0.2761 | |
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| 1.1196 | 0.44 | 2500 | 0.2444 | 0.2367 | |
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| 1.0712 | 0.53 | 3000 | 0.2324 | 0.2132 | |
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| 1.052 | 0.62 | 3500 | 0.2173 | 0.2032 | |
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| 1.2813 | 2.13 | 4000 | 0.3326 | 0.2099 | |
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| 1.2365 | 2.4 | 4500 | 0.3224 | 0.2003 | |
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| 1.2193 | 2.66 | 5000 | 0.3198 | 0.1957 | |
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| 1.2072 | 2.93 | 5500 | 0.3063 | 0.1933 | |
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| 1.213 | 3.2 | 6000 | 0.3051 | 0.1980 | |
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| 1.2074 | 3.46 | 6500 | 0.3012 | 0.1879 | |
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| 1.1918 | 3.73 | 7000 | 0.2947 | 0.1829 | |
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| 1.1893 | 4.0 | 7500 | 0.2895 | 0.1807 | |
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| 1.1751 | 4.26 | 8000 | 0.2878 | 0.1776 | |
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| 1.1628 | 4.53 | 8500 | 0.2835 | 0.1731 | |
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| 1.1577 | 4.79 | 9000 | 0.2816 | 0.1761 | |
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| 1.1448 | 5.06 | 9500 | 0.2757 | 0.1740 | |
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| 1.1407 | 5.33 | 10000 | 0.2768 | 0.1798 | |
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| 1.1401 | 5.59 | 10500 | 0.2780 | 0.1816 | |
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| 1.1333 | 5.86 | 11000 | 0.2748 | 0.1750 | |
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| 1.1571 | 6.13 | 11500 | 0.2808 | 0.1708 | |
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| 1.1505 | 6.39 | 12000 | 0.2726 | 0.1692 | |
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| 1.1519 | 6.66 | 12500 | 0.2749 | 0.1654 | |
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| 1.136 | 6.93 | 13000 | 0.2765 | 0.1643 | |
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| 1.1326 | 7.19 | 13500 | 0.2706 | 0.1668 | |
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| 1.1342 | 7.46 | 14000 | 0.2665 | 0.1638 | |
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| 1.1286 | 7.72 | 14500 | 0.2669 | 0.1636 | |
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| 1.1243 | 7.99 | 15000 | 0.2619 | 0.1623 | |
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| 1.1173 | 8.26 | 15500 | 0.2652 | 0.1604 | |
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| 1.1129 | 8.52 | 16000 | 0.2610 | 0.1598 | |
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| 1.1091 | 8.79 | 16500 | 0.2608 | 0.1584 | |
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| 1.1053 | 9.06 | 17000 | 0.2633 | 0.1664 | |
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| 1.1004 | 9.32 | 17500 | 0.2594 | 0.1662 | |
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| 1.0995 | 9.59 | 18000 | 0.2623 | 0.1569 | |
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| 1.0964 | 9.86 | 18500 | 0.2624 | 0.1597 | |
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| 1.09 | 10.12 | 19000 | 0.2577 | 0.1578 | |
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| 1.089 | 10.39 | 19500 | 0.2574 | 0.1531 | |
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| 1.0864 | 10.66 | 20000 | 0.2556 | 0.1546 | |
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| 1.0806 | 10.92 | 20500 | 0.2548 | 0.1583 | |
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| 1.0842 | 11.19 | 21000 | 0.2550 | 0.1542 | |
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| 1.0805 | 11.45 | 21500 | 0.2561 | 0.1524 | |
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| 1.0722 | 11.72 | 22000 | 0.2540 | 0.1566 | |
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| 1.0763 | 11.99 | 22500 | 0.2549 | 0.1572 | |
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| 1.0835 | 12.25 | 23000 | 0.2586 | 0.1521 | |
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| 1.0883 | 12.52 | 23500 | 0.2583 | 0.1519 | |
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| 1.0888 | 12.79 | 24000 | 0.2551 | 0.1582 | |
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| 1.0933 | 13.05 | 24500 | 0.2628 | 0.1537 | |
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| 1.0799 | 13.32 | 25000 | 0.2600 | 0.1508 | |
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| 1.0804 | 13.59 | 25500 | 0.2620 | 0.1475 | |
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| 1.0814 | 13.85 | 26000 | 0.2537 | 0.1517 | |
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| 1.0693 | 14.12 | 26500 | 0.2560 | 0.1542 | |
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| 1.0724 | 14.38 | 27000 | 0.2540 | 0.1574 | |
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| 1.0704 | 14.65 | 27500 | 0.2548 | 0.1626 | |
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| 1.0729 | 14.92 | 28000 | 0.2548 | 0.1601 | |
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| 1.0724 | 15.18 | 28500 | 0.2511 | 0.1512 | |
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| 1.0655 | 15.45 | 29000 | 0.2498 | 0.1490 | |
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| 1.0608 | 15.98 | 30000 | 0.2487 | 0.1481 | |
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| 1.0541 | 16.52 | 31000 | 0.2468 | 0.1504 | |
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| 1.0584 | 17.05 | 32000 | 0.2467 | 0.1493 | |
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| 1.0507 | 17.58 | 33000 | 0.2481 | 0.1517 | |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.0 |
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# Thanks |
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Want to thank both [@ccoreilly](https://github.com/ccoreilly) and [@gullabi](https://github.com/gullabi) who have contributed with their own resources and knowledge into making this model possible. |
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