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rubert-base-cased_pos

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  1. README.md +136 -0
  2. config.json +42 -0
  3. model.safetensors +3 -0
  4. runs/May07_08-09-13_cc926814451a/events.out.tfevents.1715069354.cc926814451a.35.2 +3 -0
  5. runs/May07_08-09-13_cc926814451a/events.out.tfevents.1715070556.cc926814451a.35.3 +3 -0
  6. special_tokens_map.json +7 -0
  7. tokenizer.json +0 -0
  8. tokenizer_config.json +58 -0
  9. training_args.bin +3 -0
  10. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ base_model: DeepPavlov/rubert-base-cased
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: rubert-base-cased_pos
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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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+ # rubert-base-cased_pos
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+
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+ This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4707
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+ - Precision: 0.5814
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+ - Recall: 0.5824
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+ - F1: 0.5819
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+ - Accuracy: 0.9021
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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: 5e-05
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+ - train_batch_size: 4
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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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+ - num_epochs: 100
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.11 | 50 | 0.6626 | 0.0119 | 0.0019 | 0.0033 | 0.7605 |
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+ | No log | 2.22 | 100 | 0.5508 | 0.0187 | 0.0193 | 0.0190 | 0.7994 |
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+ | No log | 3.33 | 150 | 0.4129 | 0.1039 | 0.1139 | 0.1087 | 0.8451 |
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+ | No log | 4.44 | 200 | 0.3683 | 0.1473 | 0.1448 | 0.1461 | 0.8555 |
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+ | No log | 5.56 | 250 | 0.3231 | 0.2346 | 0.2934 | 0.2607 | 0.8687 |
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+ | No log | 6.67 | 300 | 0.3154 | 0.3034 | 0.4498 | 0.3624 | 0.8752 |
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+ | No log | 7.78 | 350 | 0.2665 | 0.3306 | 0.4595 | 0.3845 | 0.8931 |
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+ | No log | 8.89 | 400 | 0.2724 | 0.3532 | 0.5734 | 0.4371 | 0.8929 |
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+ | No log | 10.0 | 450 | 0.2694 | 0.4179 | 0.6042 | 0.4941 | 0.8997 |
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+ | 0.3748 | 11.11 | 500 | 0.2984 | 0.4609 | 0.5347 | 0.4951 | 0.9051 |
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+ | 0.3748 | 12.22 | 550 | 0.2823 | 0.4063 | 0.6448 | 0.4985 | 0.8926 |
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+ | 0.3748 | 13.33 | 600 | 0.2894 | 0.4514 | 0.5463 | 0.4943 | 0.9022 |
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+ | 0.3748 | 14.44 | 650 | 0.3150 | 0.5043 | 0.5676 | 0.5341 | 0.9043 |
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+ | 0.3748 | 15.56 | 700 | 0.3608 | 0.4828 | 0.5965 | 0.5337 | 0.9018 |
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+ | 0.3748 | 16.67 | 750 | 0.4073 | 0.4824 | 0.6332 | 0.5476 | 0.9003 |
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+ | 0.3748 | 17.78 | 800 | 0.4018 | 0.5271 | 0.5811 | 0.5528 | 0.9083 |
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+ | 0.3748 | 18.89 | 850 | 0.4261 | 0.528 | 0.6371 | 0.5774 | 0.9047 |
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+ | 0.3748 | 20.0 | 900 | 0.4229 | 0.5187 | 0.6429 | 0.5741 | 0.9049 |
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+ | 0.3748 | 21.11 | 950 | 0.4429 | 0.4511 | 0.6506 | 0.5328 | 0.8942 |
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+ | 0.0787 | 22.22 | 1000 | 0.3901 | 0.5289 | 0.6004 | 0.5624 | 0.9102 |
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+ | 0.0787 | 23.33 | 1050 | 0.3880 | 0.5 | 0.5965 | 0.5440 | 0.9122 |
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+ | 0.0787 | 24.44 | 1100 | 0.3993 | 0.5462 | 0.6042 | 0.5738 | 0.9109 |
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+ | 0.0787 | 25.56 | 1150 | 0.5128 | 0.5087 | 0.5097 | 0.5092 | 0.9010 |
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+ | 0.0787 | 26.67 | 1200 | 0.3716 | 0.5492 | 0.6139 | 0.5798 | 0.9144 |
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+ | 0.0787 | 27.78 | 1250 | 0.4023 | 0.5087 | 0.6197 | 0.5587 | 0.9093 |
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+ | 0.0787 | 28.89 | 1300 | 0.4473 | 0.5461 | 0.6178 | 0.5797 | 0.9106 |
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+ | 0.0787 | 30.0 | 1350 | 0.4449 | 0.5739 | 0.5772 | 0.5756 | 0.9067 |
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+ | 0.0787 | 31.11 | 1400 | 0.5749 | 0.5466 | 0.5656 | 0.5560 | 0.9025 |
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+ | 0.0787 | 32.22 | 1450 | 0.4480 | 0.5571 | 0.6120 | 0.5833 | 0.9125 |
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+ | 0.0352 | 33.33 | 1500 | 0.4459 | 0.4877 | 0.6120 | 0.5428 | 0.9004 |
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+ | 0.0352 | 34.44 | 1550 | 0.4265 | 0.5554 | 0.6293 | 0.5900 | 0.9148 |
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+ | 0.0352 | 35.56 | 1600 | 0.3678 | 0.5983 | 0.5347 | 0.5647 | 0.9031 |
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+ | 0.0352 | 36.67 | 1650 | 0.4182 | 0.5777 | 0.6100 | 0.5934 | 0.9120 |
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+ | 0.0352 | 37.78 | 1700 | 0.4270 | 0.5767 | 0.6390 | 0.6062 | 0.9146 |
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+ | 0.0352 | 38.89 | 1750 | 0.4614 | 0.5283 | 0.6486 | 0.5823 | 0.9045 |
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+ | 0.0352 | 40.0 | 1800 | 0.4962 | 0.5553 | 0.5714 | 0.5633 | 0.9124 |
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+ | 0.0352 | 41.11 | 1850 | 0.4498 | 0.5981 | 0.6178 | 0.6078 | 0.9169 |
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+ | 0.0352 | 42.22 | 1900 | 0.4311 | 0.4920 | 0.6564 | 0.5624 | 0.9009 |
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+ | 0.0352 | 43.33 | 1950 | 0.5744 | 0.5065 | 0.6023 | 0.5503 | 0.8935 |
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+ | 0.0269 | 44.44 | 2000 | 0.4810 | 0.5325 | 0.5849 | 0.5575 | 0.9093 |
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+ | 0.0269 | 45.56 | 2050 | 0.5074 | 0.5918 | 0.5541 | 0.5723 | 0.9064 |
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+ | 0.0269 | 46.67 | 2100 | 0.5291 | 0.5081 | 0.6622 | 0.5750 | 0.8938 |
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+ | 0.0269 | 47.78 | 2150 | 0.4815 | 0.5904 | 0.6429 | 0.6155 | 0.9092 |
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+ | 0.0269 | 48.89 | 2200 | 0.5512 | 0.6294 | 0.5869 | 0.6074 | 0.9126 |
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+ | 0.0269 | 50.0 | 2250 | 0.4686 | 0.5876 | 0.6023 | 0.5949 | 0.9094 |
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+ | 0.0269 | 51.11 | 2300 | 0.4961 | 0.5942 | 0.5965 | 0.5954 | 0.9135 |
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+ | 0.0269 | 52.22 | 2350 | 0.5033 | 0.5471 | 0.6390 | 0.5895 | 0.9046 |
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+ | 0.0269 | 53.33 | 2400 | 0.5583 | 0.6308 | 0.5212 | 0.5708 | 0.9021 |
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+ | 0.0269 | 54.44 | 2450 | 0.4128 | 0.5904 | 0.5927 | 0.5915 | 0.9003 |
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+ | 0.0268 | 55.56 | 2500 | 0.4908 | 0.5219 | 0.5985 | 0.5576 | 0.9059 |
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+ | 0.0268 | 56.67 | 2550 | 0.4669 | 0.5068 | 0.6506 | 0.5697 | 0.9004 |
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+ | 0.0268 | 57.78 | 2600 | 0.4738 | 0.5692 | 0.6351 | 0.6004 | 0.9004 |
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+ | 0.0268 | 58.89 | 2650 | 0.4933 | 0.6197 | 0.6197 | 0.6197 | 0.9185 |
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+ | 0.0268 | 60.0 | 2700 | 0.4623 | 0.6060 | 0.6236 | 0.6147 | 0.9141 |
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+ | 0.0268 | 61.11 | 2750 | 0.4472 | 0.5981 | 0.6178 | 0.6078 | 0.9163 |
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+ | 0.0268 | 62.22 | 2800 | 0.4988 | 0.6410 | 0.6274 | 0.6341 | 0.9132 |
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+ | 0.0268 | 63.33 | 2850 | 0.4900 | 0.6176 | 0.5830 | 0.5998 | 0.9065 |
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+ | 0.0268 | 64.44 | 2900 | 0.4380 | 0.6410 | 0.6100 | 0.6251 | 0.9143 |
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+ | 0.0268 | 65.56 | 2950 | 0.5097 | 0.6115 | 0.6564 | 0.6331 | 0.9130 |
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+ | 0.0251 | 66.67 | 3000 | 0.3672 | 0.5845 | 0.6409 | 0.6114 | 0.9088 |
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+ | 0.0251 | 67.78 | 3050 | 0.4630 | 0.5213 | 0.6371 | 0.5734 | 0.9106 |
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+ | 0.0251 | 68.89 | 3100 | 0.4418 | 0.6375 | 0.6042 | 0.6204 | 0.9134 |
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+ | 0.0251 | 70.0 | 3150 | 0.4648 | 0.5705 | 0.6718 | 0.6170 | 0.9122 |
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+ | 0.0251 | 71.11 | 3200 | 0.4314 | 0.6388 | 0.5907 | 0.6138 | 0.9138 |
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+ | 0.0251 | 72.22 | 3250 | 0.4000 | 0.5575 | 0.6178 | 0.5861 | 0.9121 |
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+ | 0.0251 | 73.33 | 3300 | 0.3548 | 0.5039 | 0.6216 | 0.5566 | 0.9145 |
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+ | 0.0251 | 74.44 | 3350 | 0.4886 | 0.6616 | 0.5849 | 0.6209 | 0.9124 |
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+ | 0.0251 | 75.56 | 3400 | 0.5781 | 0.4402 | 0.6602 | 0.5282 | 0.8803 |
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+ | 0.0251 | 76.67 | 3450 | 0.3379 | 0.5704 | 0.6178 | 0.5931 | 0.9088 |
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+ | 0.0271 | 77.78 | 3500 | 0.4527 | 0.5303 | 0.6429 | 0.5812 | 0.9016 |
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+ | 0.0271 | 78.89 | 3550 | 0.5293 | 0.6211 | 0.5792 | 0.5994 | 0.9085 |
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+ | 0.0271 | 80.0 | 3600 | 0.6486 | 0.3684 | 0.6351 | 0.4663 | 0.8263 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.38.2
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+ - Pytorch 2.1.2
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+ - Datasets 2.1.0
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+ - Tokenizers 0.15.2
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