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cantillation
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Teamim-small_WeightDecay-0.05_Augmented_New-Data_nusach-yerushalmi_date-24-07-2024

Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Hebrew
whisper
hf-asr-leaderboard
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
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  2. generation_config.json +264 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: openai/whisper-small
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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: Teamim-small_WeightDecay-0.05_Augmented_New-Data_date-24-07-2024
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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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+ # Teamim-small_WeightDecay-0.05_Augmented_New-Data_date-24-07-2024
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+
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+ This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2759
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+ - Wer: 12.0860
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+ - Avg Precision Exact: 0.9045
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+ - Avg Recall Exact: 0.9054
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+ - Avg F1 Exact: 0.9045
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+ - Avg Precision Letter Shift: 0.9223
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+ - Avg Recall Letter Shift: 0.9233
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+ - Avg F1 Letter Shift: 0.9224
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+ - Avg Precision Word Level: 0.9250
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+ - Avg Recall Word Level: 0.9259
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+ - Avg F1 Word Level: 0.9250
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+ - Avg Precision Word Shift: 0.9777
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+ - Avg Recall Word Shift: 0.9785
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+ - Avg F1 Word Shift: 0.9777
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+ - Precision Median Exact: 1.0
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+ - Recall Median Exact: 1.0
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+ - F1 Median Exact: 1.0
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+ - Precision Max Exact: 1.0
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+ - Recall Max Exact: 1.0
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+ - F1 Max Exact: 1.0
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+ - Precision Min Exact: 0.0
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+ - Recall Min Exact: 0.0
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+ - F1 Min Exact: 0.0
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+ - Precision Min Letter Shift: 0.0
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+ - Recall Min Letter Shift: 0.0
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+ - F1 Min Letter Shift: 0.0
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+ - Precision Min Word Level: 0.0
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+ - Recall Min Word Level: 0.0
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+ - F1 Min Word Level: 0.0
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+ - Precision Min Word Shift: 0.0
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+ - Recall Min Word Shift: 0.0
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+ - F1 Min Word Shift: 0.0
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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: 1e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 32
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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: 500
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+ - training_steps: 200000
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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 | Avg Precision Exact | Avg Recall Exact | Avg F1 Exact | Avg Precision Letter Shift | Avg Recall Letter Shift | Avg F1 Letter Shift | Avg Precision Word Level | Avg Recall Word Level | Avg F1 Word Level | Avg Precision Word Shift | Avg Recall Word Shift | Avg F1 Word Shift | Precision Median Exact | Recall Median Exact | F1 Median Exact | Precision Max Exact | Recall Max Exact | F1 Max Exact | Precision Min Exact | Recall Min Exact | F1 Min Exact | Precision Min Letter Shift | Recall Min Letter Shift | F1 Min Letter Shift | Precision Min Word Level | Recall Min Word Level | F1 Min Word Level | Precision Min Word Shift | Recall Min Word Shift | F1 Min Word Shift |
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+ |:-------------:|:-------:|:------:|:---------------:|:--------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|
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+ | No log | 0.0004 | 1 | 6.8177 | 106.5214 | 0.0004 | 0.0012 | 0.0006 | 0.0038 | 0.0036 | 0.0033 | 0.0030 | 0.0121 | 0.0043 | 0.0322 | 0.0342 | 0.0300 | 0.0 | 0.0 | 0.0 | 0.0909 | 0.3333 | 0.1429 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0059 | 3.7023 | 10000 | 0.1748 | 15.8840 | 0.8772 | 0.8813 | 0.8786 | 0.9013 | 0.9056 | 0.9028 | 0.9063 | 0.9103 | 0.9077 | 0.9648 | 0.9693 | 0.9663 | 0.9286 | 0.9375 | 0.9474 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0045 | 7.4047 | 20000 | 0.2047 | 15.1038 | 0.8686 | 0.8670 | 0.8673 | 0.8906 | 0.8892 | 0.8894 | 0.8952 | 0.8935 | 0.8938 | 0.9722 | 0.9711 | 0.9710 | 0.9375 | 0.9333 | 0.9524 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.001 | 11.1070 | 30000 | 0.2024 | 13.8083 | 0.8862 | 0.8876 | 0.8863 | 0.9076 | 0.9094 | 0.9080 | 0.9109 | 0.9127 | 0.9113 | 0.9743 | 0.9767 | 0.9749 | 1.0 | 1.0 | 0.9600 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.001 | 14.8093 | 40000 | 0.2188 | 13.8083 | 0.8924 | 0.8918 | 0.8916 | 0.9125 | 0.9118 | 0.9116 | 0.9166 | 0.9156 | 0.9155 | 0.9733 | 0.9730 | 0.9726 | 1.0 | 1.0 | 0.9630 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0005 | 18.5117 | 50000 | 0.2256 | 13.6464 | 0.8921 | 0.8937 | 0.8924 | 0.9131 | 0.9148 | 0.9135 | 0.9161 | 0.9176 | 0.9164 | 0.9760 | 0.9774 | 0.9762 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0012 | 22.2140 | 60000 | 0.2194 | 12.8515 | 0.8896 | 0.8917 | 0.8902 | 0.9089 | 0.9110 | 0.9095 | 0.9116 | 0.9138 | 0.9122 | 0.9748 | 0.9780 | 0.9759 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0006 | 25.9163 | 70000 | 0.2265 | 13.0870 | 0.8981 | 0.9013 | 0.8992 | 0.9191 | 0.9224 | 0.9203 | 0.9219 | 0.9249 | 0.9229 | 0.9756 | 0.9776 | 0.9761 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0001 | 29.6187 | 80000 | 0.2249 | 13.0870 | 0.8938 | 0.8961 | 0.8945 | 0.9139 | 0.9163 | 0.9146 | 0.9169 | 0.9191 | 0.9175 | 0.9749 | 0.9764 | 0.9752 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0001 | 33.3210 | 90000 | 0.2379 | 13.2342 | 0.8960 | 0.8987 | 0.8969 | 0.9160 | 0.9189 | 0.9169 | 0.9197 | 0.9224 | 0.9206 | 0.9759 | 0.9780 | 0.9764 | 1.0 | 1.0 | 0.9697 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0 | 37.0233 | 100000 | 0.2302 | 13.1312 | 0.8910 | 0.8958 | 0.8930 | 0.9121 | 0.9171 | 0.9142 | 0.9149 | 0.9195 | 0.9167 | 0.9742 | 0.9786 | 0.9759 | 1.0 | 1.0 | 0.9677 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0004 | 40.7257 | 110000 | 0.2294 | 12.9987 | 0.9032 | 0.9028 | 0.9025 | 0.9220 | 0.9216 | 0.9213 | 0.9255 | 0.9249 | 0.9247 | 0.9762 | 0.9773 | 0.9763 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0001 | 44.4280 | 120000 | 0.2322 | 12.6601 | 0.9038 | 0.9045 | 0.9037 | 0.9234 | 0.9242 | 0.9233 | 0.9262 | 0.9270 | 0.9262 | 0.9766 | 0.9784 | 0.9770 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0 | 48.1303 | 130000 | 0.2362 | 12.5129 | 0.9054 | 0.9058 | 0.9051 | 0.9241 | 0.9247 | 0.9239 | 0.9277 | 0.9284 | 0.9276 | 0.9763 | 0.9777 | 0.9766 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0 | 51.8327 | 140000 | 0.2430 | 13.1753 | 0.8973 | 0.8993 | 0.8978 | 0.9184 | 0.9205 | 0.9189 | 0.9216 | 0.9237 | 0.9221 | 0.9766 | 0.9783 | 0.9770 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0005 | 55.5350 | 150000 | 0.2325 | 12.7926 | 0.9032 | 0.9032 | 0.9028 | 0.9226 | 0.9228 | 0.9223 | 0.9251 | 0.9252 | 0.9247 | 0.9781 | 0.9785 | 0.9779 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0 | 59.2373 | 160000 | 0.2428 | 12.2332 | 0.9090 | 0.9104 | 0.9093 | 0.9275 | 0.9289 | 0.9278 | 0.9301 | 0.9315 | 0.9304 | 0.9773 | 0.9791 | 0.9778 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0 | 62.9397 | 170000 | 0.2499 | 12.1301 | 0.9067 | 0.9081 | 0.9070 | 0.9246 | 0.9261 | 0.9249 | 0.9273 | 0.9286 | 0.9275 | 0.9775 | 0.9794 | 0.9780 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0003 | 66.6420 | 180000 | 0.2572 | 12.2185 | 0.9050 | 0.9049 | 0.9045 | 0.9238 | 0.9238 | 0.9234 | 0.9265 | 0.9263 | 0.9260 | 0.9785 | 0.9784 | 0.9780 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0 | 70.3443 | 190000 | 0.2704 | 12.1449 | 0.9058 | 0.9068 | 0.9059 | 0.9237 | 0.9247 | 0.9238 | 0.9263 | 0.9273 | 0.9264 | 0.9775 | 0.9787 | 0.9777 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.0 | 74.0466 | 200000 | 0.2759 | 12.0860 | 0.9045 | 0.9054 | 0.9045 | 0.9223 | 0.9233 | 0.9224 | 0.9250 | 0.9259 | 0.9250 | 0.9777 | 0.9785 | 0.9777 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.2
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+ - Pytorch 2.2.1
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1
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