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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- en-atc |
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- en |
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- generated_from_trainer |
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datasets: |
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- Jzuluaga/uwb_atcc |
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metrics: |
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- wer |
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base_model: facebook/wav2vec2-large-960h-lv60-self |
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model-index: |
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- name: wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Speech Recognition |
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dataset: |
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name: UWB-ATCC dataset (Air Traffic Control Communications) |
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type: Jzuluaga/uwb_atcc |
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config: test |
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split: test |
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metrics: |
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- type: wer |
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value: 17.2 |
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name: TEST WER |
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verified: false |
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- type: wer |
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value: 13.72 |
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name: TEST WER (+LM) |
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verified: false |
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- task: |
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type: automatic-speech-recognition |
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name: Speech Recognition |
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dataset: |
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name: ATCOSIM corpus (Air Traffic Control Communications) |
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type: Jzuluaga/atcosim_corpus |
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config: test |
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split: test |
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metrics: |
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- type: wer |
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value: 15.31 |
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name: TEST WER |
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verified: false |
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- type: wer |
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value: 11.88 |
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name: TEST WER (+LM) |
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verified: false |
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--- |
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# wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc |
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This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the [UWB-ATCC corpus](https://huggingface.co/datasets/Jzuluaga/uwb_atcc). |
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<a href="https://colab.research.google.com/github/idiap/w2v2-air-traffic/blob/main/src/eval_xlsr_atc_model.ipynb"> |
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<img alt="GitHub" src="https://colab.research.google.com/assets/colab-badge.svg\"> |
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</a> |
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<a href="https://github.com/idiap/w2v2-air-traffic"> |
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<img alt="GitHub" src="https://img.shields.io/badge/GitHub-Open%20source-green\"> |
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</a> |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7287 |
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- Wer: 0.1756 |
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Paper: [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822). |
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Authors: Juan Zuluaga-Gomez, Amrutha Prasad, Iuliia Nigmatulina, Saeed Sarfjoo, Petr Motlicek, Matthias Kleinert, Hartmut Helmke, Oliver Ohneiser, Qingran Zhan |
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Abstract: Recent work on self-supervised pre-training focus</b> on leveraging large-scale unlabeled speech data to build robust end-to-end (E2E)acoustic models (AM) that can be later fine-tuned on downstream tasks e.g., automatic speech recognition (ASR). Yet, few works investigated the impact on performance when the data properties substantially differ between the pre-training and fine-tuning phases, termed domain shift. We target this scenario by analyzing the robustness of Wav2Vec 2.0 and XLS-R models on downstream ASR for a completely unseen domain, air traffic control (ATC) communications. We benchmark these two models on several open-source and challenging ATC databases with signal-to-noise ratio between 5 and 20 dB. Relative word error rate (WER) reductions between 20% to 40% are obtained in comparison to hybrid-based ASR baselines by only fine-tuning E2E acoustic models with a smaller fraction of labeled data. We analyze WERs on the low-resource scenario and gender bias carried by one ATC dataset. |
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Code — GitHub repository: https://github.com/idiap/w2v2-air-traffic |
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## Usage |
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You can use our Google Colab notebook to run and evaluate our model: https://github.com/idiap/w2v2-air-traffic/blob/master/src/eval_xlsr_atc_model.ipynb |
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## Intended uses & limitations |
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This model was fine-tuned on air traffic control data. We don't expect that it keeps the same performance on some others datasets, e.g., LibriSpeech or CommonVoice. |
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## Training and evaluation data |
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See Table 1 (page 3) in our paper: [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822). We described there the partitions of how to use our model. |
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- We use the UWB-ATCC corpus to fine-tune this model. You can download the raw data here: https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0 |
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- However, do not worry, we have prepared the database in `Datasets format`. Here, [UWB-ATCC corpus on HuggingFace](https://huggingface.co/datasets/Jzuluaga/uwb_atcc). You can scroll and check the train/test partitions, and even listen to some audios. |
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- If you want to prepare a database in HuggingFace format, you can follow the data loader script in: [data_loader_atc.py](https://huggingface.co/datasets/Jzuluaga/uwb_atcc/blob/main/atc_data_loader.py). |
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- |
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## Writing your own inference script |
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If you use language model, you need to install the KenLM bindings with: |
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```bash |
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conda activate your_environment |
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pip install https://github.com/kpu/kenlm/archive/master.zip |
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``` |
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The snippet of code: |
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```python |
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from datasets import load_dataset, load_metric, Audio |
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import torch |
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from transformers import AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM |
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import torchaudio.functional as F |
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USE_LM = False |
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DATASET_ID = "Jzuluaga/uwb_atcc" |
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MODEL_ID = "Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc" |
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# 1. Load the dataset |
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# we only load the 'test' partition, however, if you want to load the 'train' partition, you can change it accordingly |
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uwb_atcc_corpus_test = load_dataset(DATASET_ID, "test", split="test") |
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# 2. Load the model |
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model = AutoModelForCTC.from_pretrained(MODEL_ID) |
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# 3. Load the processors, we offer support with LM, which should yield better resutls |
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if USE_LM: |
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processor = Wav2Vec2ProcessorWithLM.from_pretrained(MODEL_ID) |
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else: |
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processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) |
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# 4. Format the test sample |
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sample = next(iter(uwb_atcc_corpus_test)) |
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file_sampling_rate = sample['audio']['sampling_rate'] |
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# resample if neccessary |
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if file_sampling_rate != 16000: |
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resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), file_sampling_rate, 16000).numpy() |
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else: |
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resampled_audio = torch.tensor(sample["audio"]["array"]).numpy() |
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input_values = processor(resampled_audio, return_tensors="pt").input_values |
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# 5. Run the forward pass in the model |
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with torch.no_grad(): |
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logits = model(input_values).logits |
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# get the transcription with processor |
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if USE_LM: |
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transcription = processor.batch_decode(logits.numpy()).text |
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else: |
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pred_ids = torch.argmax(logits, dim=-1) |
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transcription = processor.batch_decode(pred_ids) |
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# print the output |
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print(transcription) |
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``` |
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# Cite us |
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If you use this code for your research, please cite our paper with: |
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``` |
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@article{zuluaga2022how, |
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title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications}, |
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author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others}, |
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journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, |
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year={2022} |
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} |
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``` |
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and, |
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``` |
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@article{zuluaga2022bertraffic, |
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title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications}, |
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author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others}, |
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journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, |
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year={2022} |
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} |
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``` |
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and, |
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``` |
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@article{zuluaga2022atco2, |
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title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications}, |
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author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others}, |
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journal={arXiv preprint arXiv:2211.04054}, |
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year={2022} |
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} |
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``` |
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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: 0.0001 |
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- train_batch_size: 24 |
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- eval_batch_size: 12 |
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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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- lr_scheduler_warmup_steps: 1000 |
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- training_steps: 10000 |
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- mixed_precision_training: Native AMP |
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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 | 1.06 | 500 | 2.9016 | 0.9995 | |
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| 2.877 | 2.12 | 1000 | 0.9812 | 0.3485 | |
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| 2.877 | 3.18 | 1500 | 0.7842 | 0.2732 | |
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| 0.7834 | 4.25 | 2000 | 0.6962 | 0.2192 | |
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| 0.7834 | 5.31 | 2500 | 0.6527 | 0.2042 | |
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| 0.6084 | 6.37 | 3000 | 0.6220 | 0.1972 | |
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| 0.6084 | 7.43 | 3500 | 0.6442 | 0.1934 | |
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| 0.5147 | 8.49 | 4000 | 0.6793 | 0.1950 | |
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| 0.5147 | 9.55 | 4500 | 0.6432 | 0.1920 | |
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| 0.4566 | 10.62 | 5000 | 0.6605 | 0.1853 | |
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| 0.4566 | 11.68 | 5500 | 0.6393 | 0.1866 | |
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| 0.4155 | 12.74 | 6000 | 0.6918 | 0.1803 | |
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| 0.4155 | 13.8 | 6500 | 0.6514 | 0.1791 | |
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| 0.372 | 14.86 | 7000 | 0.7010 | 0.1851 | |
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| 0.372 | 15.92 | 7500 | 0.6824 | 0.1786 | |
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| 0.3368 | 16.99 | 8000 | 0.6895 | 0.1780 | |
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| 0.3368 | 18.05 | 8500 | 0.7150 | 0.1759 | |
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| 0.3244 | 19.11 | 9000 | 0.7141 | 0.1759 | |
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| 0.3244 | 20.17 | 9500 | 0.7225 | 0.1756 | |
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| 0.2981 | 21.23 | 10000 | 0.7287 | 0.1756 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.2 |
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