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wav2vec2-base-word-by-word-quran-asr

Automatic Speech Recognition
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wav2vec2
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wav2vec2-base-word-by-word-quran-asr

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  1. README.md +215 -199
  2. config.json +118 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +0 -1
  5. runs/Jul21_18-09-45_Hamza/events.out.tfevents.1721567495.Hamza.23316.1 +3 -0
  6. runs/Jul21_18-18-23_Hamza/events.out.tfevents.1721567911.Hamza.23316.2 +3 -0
  7. runs/Jul21_18-32-35_Hamza/events.out.tfevents.1721568762.Hamza.23316.3 +3 -0
  8. runs/Jul21_18-35-07_Hamza/events.out.tfevents.1721568914.Hamza.23316.4 +3 -0
  9. runs/Jul21_18-36-23_Hamza/events.out.tfevents.1721568989.Hamza.23316.5 +3 -0
  10. training_args.bin +3 -0
README.md CHANGED
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- ---
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- library_name: transformers
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- tags: []
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- ---
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-
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- # Model Card for Model ID
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-
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- <!-- Provide a quick summary of what the model is/does. -->
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-
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-
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- ## Model Details
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- ### Model Description
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- ## Uses
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- ### Direct Use
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- ## Bias, Risks, and Limitations
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- ### Recommendations
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- ## Training Details
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- ### Training Data
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- ## Evaluation
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- [More Information Needed]
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- #### Factors
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- #### Metrics
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- ## Environmental Impact
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- ## Glossary [optional]
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- ## More Information [optional]
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- ## Model Card Contact
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+ ---
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+ license: apache-2.0
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+ base_model: facebook/wav2vec2-base
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - wer
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+ model-index:
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+ - name: wav2vec2-base-word-by-word-quran-asr
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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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+ # wav2vec2-base-word-by-word-quran-asr
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+
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+ This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0415
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+ - Wer: 0.0790
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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: 0.0001
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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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+ - lr_scheduler_warmup_steps: 1000
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+ - num_epochs: 20
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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 | Wer |
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+ |:-------------:|:-------:|:-----:|:---------------:|:------:|
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+ | 13.139 | 0.1291 | 500 | 3.2031 | 1.0 |
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+ | 1.9653 | 0.2583 | 1000 | 0.5466 | 0.7806 |
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+ | 0.4997 | 0.3874 | 1500 | 0.2171 | 0.3470 |
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+ | 0.3479 | 0.5165 | 2000 | 0.1718 | 0.2932 |
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+ | 0.2649 | 0.6457 | 2500 | 0.1434 | 0.2425 |
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+ | 0.2554 | 0.7748 | 3000 | 0.1350 | 0.2478 |
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+ | 0.2199 | 0.9039 | 3500 | 0.1047 | 0.1766 |
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+ | 0.201 | 1.0331 | 4000 | 0.1079 | 0.1690 |
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+ | 0.1832 | 1.1622 | 4500 | 0.0981 | 0.1637 |
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+ | 0.1728 | 1.2913 | 5000 | 0.0984 | 0.1594 |
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+ | 0.1608 | 1.4205 | 5500 | 0.0890 | 0.1598 |
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+ | 0.1602 | 1.5496 | 6000 | 0.0786 | 0.1461 |
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+ | 0.1565 | 1.6787 | 6500 | 0.0795 | 0.1315 |
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+ | 0.1579 | 1.8079 | 7000 | 0.0888 | 0.1326 |
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+ | 0.1537 | 1.9370 | 7500 | 0.0835 | 0.1377 |
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+ | 0.1446 | 2.0661 | 8000 | 0.0674 | 0.1225 |
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+ | 0.1313 | 2.1952 | 8500 | 0.0706 | 0.1255 |
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+ | 0.1267 | 2.3244 | 9000 | 0.0658 | 0.1243 |
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+ | 0.1385 | 2.4535 | 9500 | 0.0624 | 0.1170 |
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+ | 0.1336 | 2.5826 | 10000 | 0.0648 | 0.1180 |
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+ | 0.1189 | 2.7118 | 10500 | 0.0661 | 0.1189 |
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+ | 0.1321 | 2.8409 | 11000 | 0.0716 | 0.1273 |
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+ | 0.1168 | 2.9700 | 11500 | 0.0636 | 0.1200 |
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+ | 0.1201 | 3.0992 | 12000 | 0.0615 | 0.1157 |
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+ | 0.1144 | 3.2283 | 12500 | 0.0643 | 0.1223 |
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+ | 0.1176 | 3.3574 | 13000 | 0.0690 | 0.1284 |
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+ | 0.1153 | 3.4866 | 13500 | 0.0634 | 0.1208 |
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+ | 0.1085 | 3.6157 | 14000 | 0.0594 | 0.1175 |
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+ | 0.1176 | 3.7448 | 14500 | 0.0633 | 0.1151 |
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+ | 0.118 | 3.8740 | 15000 | 0.0607 | 0.1062 |
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+ | 0.1082 | 4.0031 | 15500 | 0.0564 | 0.1084 |
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+ | 0.1028 | 4.1322 | 16000 | 0.0633 | 0.1215 |
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+ | 0.1047 | 4.2614 | 16500 | 0.0579 | 0.1125 |
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+ | 0.1015 | 4.3905 | 17000 | 0.0548 | 0.1098 |
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+ | 0.101 | 4.5196 | 17500 | 0.0536 | 0.1088 |
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+ | 0.0911 | 4.6488 | 18000 | 0.0526 | 0.0989 |
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+ | 0.1016 | 4.7779 | 18500 | 0.0548 | 0.1073 |
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+ | 0.0947 | 4.9070 | 19000 | 0.0521 | 0.1031 |
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+ | 0.0933 | 5.0362 | 19500 | 0.0499 | 0.1024 |
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+ | 0.0951 | 5.1653 | 20000 | 0.0545 | 0.1078 |
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+ | 0.093 | 5.2944 | 20500 | 0.0521 | 0.1020 |
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+ | 0.0888 | 5.4236 | 21000 | 0.0521 | 0.1037 |
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+ | 0.0929 | 5.5527 | 21500 | 0.0536 | 0.1076 |
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+ | 0.0983 | 5.6818 | 22000 | 0.0541 | 0.1047 |
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+ | 0.0928 | 5.8110 | 22500 | 0.0526 | 0.1033 |
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+ | 0.09 | 5.9401 | 23000 | 0.0506 | 0.1081 |
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+ | 0.089 | 6.0692 | 23500 | 0.0529 | 0.1073 |
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+ | 0.0785 | 6.1983 | 24000 | 0.0504 | 0.1015 |
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+ | 0.0799 | 6.3275 | 24500 | 0.0538 | 0.1068 |
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+ | 0.0833 | 6.4566 | 25000 | 0.0482 | 0.1027 |
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+ | 0.0824 | 6.5857 | 25500 | 0.0503 | 0.0965 |
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+ | 0.0801 | 6.7149 | 26000 | 0.0484 | 0.0962 |
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+ | 0.0787 | 6.8440 | 26500 | 0.0483 | 0.0974 |
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+ | 0.0786 | 6.9731 | 27000 | 0.0522 | 0.1008 |
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+ | 0.0812 | 7.1023 | 27500 | 0.0475 | 0.0993 |
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+ | 0.0736 | 7.2314 | 28000 | 0.0494 | 0.0932 |
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+ | 0.0778 | 7.3605 | 28500 | 0.0488 | 0.0959 |
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+ | 0.0742 | 7.4897 | 29000 | 0.0465 | 0.0921 |
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+ | 0.077 | 7.6188 | 29500 | 0.0459 | 0.0997 |
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+ | 0.0716 | 7.7479 | 30000 | 0.0466 | 0.0971 |
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+ | 0.0768 | 7.8771 | 30500 | 0.0485 | 0.1004 |
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+ | 0.0729 | 8.0062 | 31000 | 0.0479 | 0.0970 |
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+ | 0.0784 | 8.1353 | 31500 | 0.0746 | 0.1563 |
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+ | 0.0855 | 8.2645 | 32000 | 0.0513 | 0.0972 |
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+ | 0.0743 | 8.3936 | 32500 | 0.0474 | 0.0953 |
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+ | 0.0699 | 8.5227 | 33000 | 0.0457 | 0.0929 |
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+ | 0.0711 | 8.6519 | 33500 | 0.0480 | 0.0924 |
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+ | 0.0719 | 8.7810 | 34000 | 0.0455 | 0.0909 |
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+ | 0.0723 | 8.9101 | 34500 | 0.0442 | 0.0924 |
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+ | 0.0715 | 9.0393 | 35000 | 0.0453 | 0.0945 |
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+ | 0.0664 | 9.1684 | 35500 | 0.0458 | 0.0903 |
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+ | 0.0636 | 9.2975 | 36000 | 0.0450 | 0.0929 |
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+ | 0.0665 | 9.4267 | 36500 | 0.0461 | 0.0909 |
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+ | 0.0668 | 9.5558 | 37000 | 0.0477 | 0.0923 |
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+ | 0.0631 | 9.6849 | 37500 | 0.0463 | 0.0900 |
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+ | 0.0686 | 9.8140 | 38000 | 0.0481 | 0.0983 |
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+ | 0.0645 | 9.9432 | 38500 | 0.0591 | 0.0938 |
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+ | 0.0661 | 10.0723 | 39000 | 0.0464 | 0.0912 |
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+ | 0.0648 | 10.2014 | 39500 | 0.0458 | 0.0902 |
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+ | 0.0597 | 10.3306 | 40000 | 0.0460 | 0.0899 |
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+ | 0.0605 | 10.4597 | 40500 | 0.0465 | 0.0868 |
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+ | 0.0623 | 10.5888 | 41000 | 0.0471 | 0.0909 |
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+ | 0.065 | 10.7180 | 41500 | 0.0766 | 0.1173 |
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+ | 0.0674 | 10.8471 | 42000 | 0.0469 | 0.0903 |
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+ | 0.0631 | 10.9762 | 42500 | 0.0436 | 0.0905 |
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+ | 0.0596 | 11.1054 | 43000 | 0.0472 | 0.0903 |
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+ | 0.0612 | 11.2345 | 43500 | 0.0436 | 0.0869 |
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+ | 0.0598 | 11.3636 | 44000 | 0.0451 | 0.0883 |
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+ | 0.0637 | 11.4928 | 44500 | 0.0437 | 0.0909 |
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+ | 0.0556 | 11.6219 | 45000 | 0.0440 | 0.0870 |
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+ | 0.0625 | 11.7510 | 45500 | 0.0469 | 0.0941 |
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+ | 0.0573 | 11.8802 | 46000 | 0.0461 | 0.0903 |
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+ | 0.0583 | 12.0093 | 46500 | 0.0454 | 0.0901 |
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+ | 0.0587 | 12.1384 | 47000 | 0.0440 | 0.0907 |
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+ | 0.0598 | 12.2676 | 47500 | 0.0440 | 0.0878 |
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+ | 0.0578 | 12.3967 | 48000 | 0.0458 | 0.0900 |
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+ | 0.0549 | 12.5258 | 48500 | 0.0441 | 0.0863 |
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+ | 0.0505 | 12.6550 | 49000 | 0.0436 | 0.0883 |
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+ | 0.0528 | 12.7841 | 49500 | 0.0412 | 0.0855 |
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+ | 0.0498 | 12.9132 | 50000 | 0.0430 | 0.0851 |
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+ | 0.0538 | 13.0424 | 50500 | 0.0433 | 0.0856 |
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+ | 0.0503 | 13.1715 | 51000 | 0.0445 | 0.0846 |
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+ | 0.0529 | 13.3006 | 51500 | 0.0422 | 0.0830 |
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+ | 0.0513 | 13.4298 | 52000 | 0.0415 | 0.0871 |
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+ | 0.0507 | 13.5589 | 52500 | 0.0438 | 0.0862 |
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+ | 0.0507 | 13.6880 | 53000 | 0.0419 | 0.0818 |
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+ | 0.0537 | 13.8171 | 53500 | 0.0473 | 0.0870 |
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+ | 0.053 | 13.9463 | 54000 | 0.0415 | 0.0834 |
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+ | 0.0472 | 14.0754 | 54500 | 0.0427 | 0.0839 |
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+ | 0.0473 | 14.2045 | 55000 | 0.0436 | 0.0855 |
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+ | 0.0504 | 14.3337 | 55500 | 0.0418 | 0.0823 |
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+ | 0.0496 | 14.4628 | 56000 | 0.0425 | 0.0845 |
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+ | 0.0474 | 14.5919 | 56500 | 0.0432 | 0.0844 |
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+ | 0.0462 | 14.7211 | 57000 | 0.0429 | 0.0851 |
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+ | 0.0499 | 14.8502 | 57500 | 0.0426 | 0.0839 |
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+ | 0.0507 | 14.9793 | 58000 | 0.0425 | 0.0821 |
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+ | 0.0424 | 15.1085 | 58500 | 0.0442 | 0.0838 |
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+ | 0.0463 | 15.2376 | 59000 | 0.0422 | 0.0859 |
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+ | 0.0465 | 15.3667 | 59500 | 0.0439 | 0.0827 |
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+ | 0.0483 | 15.4959 | 60000 | 0.0436 | 0.0852 |
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+ | 0.0498 | 15.625 | 60500 | 0.0424 | 0.0859 |
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+ | 0.047 | 15.7541 | 61000 | 0.0424 | 0.0853 |
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+ | 0.0466 | 15.8833 | 61500 | 0.0436 | 0.0858 |
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+ | 0.0487 | 16.0124 | 62000 | 0.0423 | 0.0832 |
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+ | 0.0414 | 16.1415 | 62500 | 0.0429 | 0.0845 |
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+ | 0.0447 | 16.2707 | 63000 | 0.0418 | 0.0832 |
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+ | 0.0404 | 16.3998 | 63500 | 0.0428 | 0.0817 |
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+ | 0.0419 | 16.5289 | 64000 | 0.0428 | 0.0825 |
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+ | 0.0433 | 16.6581 | 64500 | 0.0415 | 0.0825 |
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+ | 0.0429 | 16.7872 | 65000 | 0.0423 | 0.0815 |
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+ | 0.0415 | 16.9163 | 65500 | 0.0408 | 0.0804 |
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+ | 0.0422 | 17.0455 | 66000 | 0.0424 | 0.0817 |
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+ | 0.0404 | 17.1746 | 66500 | 0.0430 | 0.0809 |
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+ | 0.0387 | 17.3037 | 67000 | 0.0424 | 0.0816 |
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+ | 0.0413 | 17.4329 | 67500 | 0.0414 | 0.0805 |
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+ | 0.039 | 17.5620 | 68000 | 0.0414 | 0.0828 |
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+ | 0.0394 | 17.6911 | 68500 | 0.0418 | 0.0799 |
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+ | 0.0419 | 17.8202 | 69000 | 0.0405 | 0.0801 |
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+ | 0.0417 | 17.9494 | 69500 | 0.0412 | 0.0793 |
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+ | 0.0383 | 18.0785 | 70000 | 0.0431 | 0.0799 |
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+ | 0.0393 | 18.2076 | 70500 | 0.0426 | 0.0817 |
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+ | 0.0364 | 18.3368 | 71000 | 0.0426 | 0.0795 |
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+ | 0.0379 | 18.4659 | 71500 | 0.0419 | 0.0799 |
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+ | 0.0359 | 18.5950 | 72000 | 0.0419 | 0.0796 |
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+ | 0.0368 | 18.7242 | 72500 | 0.0419 | 0.0794 |
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+ | 0.0394 | 18.8533 | 73000 | 0.0414 | 0.0800 |
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+ | 0.0375 | 18.9824 | 73500 | 0.0418 | 0.0791 |
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+ | 0.0381 | 19.1116 | 74000 | 0.0420 | 0.0794 |
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+ | 0.039 | 19.2407 | 74500 | 0.0415 | 0.0794 |
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+ | 0.0364 | 19.3698 | 75000 | 0.0419 | 0.0795 |
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+ | 0.0334 | 19.4990 | 75500 | 0.0421 | 0.0796 |
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+ | 0.0352 | 19.6281 | 76000 | 0.0415 | 0.0793 |
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+ | 0.0354 | 19.7572 | 76500 | 0.0416 | 0.0787 |
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+ | 0.0362 | 19.8864 | 77000 | 0.0415 | 0.0790 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.42.3
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+ - Pytorch 2.3.1+cu121
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1
config.json ADDED
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