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SimoneJLaudani
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trainer_3f

Text Classification
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distilbert
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End of training

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  1. README.md +139 -0
  2. config.json +43 -0
  3. model.safetensors +3 -0
  4. runs/Apr06_08-21-52_05814fbb5cd7/events.out.tfevents.1712391727.05814fbb5cd7.176.0 +3 -0
  5. runs/Apr06_09-06-31_05814fbb5cd7/events.out.tfevents.1712394393.05814fbb5cd7.176.1 +3 -0
  6. runs/Apr06_09-06-31_05814fbb5cd7/events.out.tfevents.1712414367.05814fbb5cd7.176.2 +3 -0
  7. special_tokens_map.json +7 -0
  8. tokenizer_config.json +57 -0
  9. training_args.bin +3 -0
  10. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: distilbert-base-uncased
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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: trainer_3f
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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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+ # trainer_3f
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+
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+ This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.4852
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+ - Precision: 0.8278
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+ - Recall: 0.8235
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+ - F1: 0.8236
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+ - Accuracy: 0.8235
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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: 8
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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: 10
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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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+ | 0.4929 | 0.14 | 30 | 0.8404 | 0.7905 | 0.7703 | 0.7635 | 0.7703 |
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+ | 0.2641 | 0.27 | 60 | 0.7850 | 0.7994 | 0.7927 | 0.7931 | 0.7927 |
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+ | 0.3163 | 0.41 | 90 | 0.8025 | 0.7997 | 0.7927 | 0.7926 | 0.7927 |
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+ | 0.2025 | 0.54 | 120 | 0.8584 | 0.8177 | 0.8067 | 0.8059 | 0.8067 |
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+ | 0.2399 | 0.68 | 150 | 0.8939 | 0.8128 | 0.8011 | 0.7993 | 0.8011 |
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+ | 0.226 | 0.81 | 180 | 0.9530 | 0.7911 | 0.7787 | 0.7799 | 0.7787 |
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+ | 0.1659 | 0.95 | 210 | 0.8972 | 0.8019 | 0.7983 | 0.7978 | 0.7983 |
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+ | 0.079 | 1.08 | 240 | 0.9426 | 0.8264 | 0.8207 | 0.8198 | 0.8207 |
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+ | 0.0125 | 1.22 | 270 | 0.9969 | 0.8282 | 0.8207 | 0.8198 | 0.8207 |
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+ | 0.057 | 1.35 | 300 | 1.1346 | 0.8074 | 0.7927 | 0.7937 | 0.7927 |
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+ | 0.0778 | 1.49 | 330 | 1.0462 | 0.8280 | 0.8235 | 0.8231 | 0.8235 |
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+ | 0.0851 | 1.62 | 360 | 1.0396 | 0.8230 | 0.8179 | 0.8176 | 0.8179 |
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+ | 0.1426 | 1.76 | 390 | 1.1215 | 0.8323 | 0.8263 | 0.8249 | 0.8263 |
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+ | 0.2026 | 1.89 | 420 | 1.1725 | 0.8020 | 0.7955 | 0.7951 | 0.7955 |
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+ | 0.1132 | 2.03 | 450 | 1.1997 | 0.8063 | 0.8039 | 0.8032 | 0.8039 |
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+ | 0.011 | 2.16 | 480 | 1.2057 | 0.8099 | 0.8039 | 0.8042 | 0.8039 |
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+ | 0.0373 | 2.3 | 510 | 1.4208 | 0.7777 | 0.7675 | 0.7649 | 0.7675 |
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+ | 0.0468 | 2.43 | 540 | 1.3124 | 0.7947 | 0.7843 | 0.7842 | 0.7843 |
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+ | 0.0434 | 2.57 | 570 | 1.3305 | 0.7972 | 0.7927 | 0.7915 | 0.7927 |
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+ | 0.1619 | 2.7 | 600 | 1.4492 | 0.7964 | 0.7843 | 0.7809 | 0.7843 |
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+ | 0.0283 | 2.84 | 630 | 1.4400 | 0.7912 | 0.7815 | 0.7772 | 0.7815 |
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+ | 0.0384 | 2.97 | 660 | 1.2395 | 0.8115 | 0.8039 | 0.8042 | 0.8039 |
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+ | 0.0484 | 3.11 | 690 | 1.2055 | 0.8280 | 0.8235 | 0.8233 | 0.8235 |
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+ | 0.0012 | 3.24 | 720 | 1.2217 | 0.8150 | 0.8123 | 0.8115 | 0.8123 |
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+ | 0.0219 | 3.38 | 750 | 1.2076 | 0.8248 | 0.8207 | 0.8203 | 0.8207 |
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+ | 0.0041 | 3.51 | 780 | 1.5603 | 0.7960 | 0.7815 | 0.7804 | 0.7815 |
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+ | 0.0044 | 3.65 | 810 | 1.3068 | 0.8158 | 0.8067 | 0.8059 | 0.8067 |
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+ | 0.0082 | 3.78 | 840 | 1.3362 | 0.8133 | 0.8067 | 0.8064 | 0.8067 |
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+ | 0.0242 | 3.92 | 870 | 1.3184 | 0.8176 | 0.8151 | 0.8150 | 0.8151 |
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+ | 0.0034 | 4.05 | 900 | 1.2882 | 0.8303 | 0.8263 | 0.8267 | 0.8263 |
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+ | 0.0004 | 4.19 | 930 | 1.2980 | 0.8222 | 0.8179 | 0.8183 | 0.8179 |
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+ | 0.0004 | 4.32 | 960 | 1.3349 | 0.8155 | 0.8123 | 0.8123 | 0.8123 |
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+ | 0.0006 | 4.46 | 990 | 1.3493 | 0.8143 | 0.8123 | 0.8119 | 0.8123 |
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+ | 0.0004 | 4.59 | 1020 | 1.3488 | 0.8255 | 0.8207 | 0.8208 | 0.8207 |
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+ | 0.0014 | 4.73 | 1050 | 1.3566 | 0.8283 | 0.8235 | 0.8233 | 0.8235 |
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+ | 0.0005 | 4.86 | 1080 | 1.2668 | 0.8375 | 0.8319 | 0.8318 | 0.8319 |
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+ | 0.0004 | 5.0 | 1110 | 1.2401 | 0.8328 | 0.8263 | 0.8263 | 0.8263 |
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+ | 0.0079 | 5.14 | 1140 | 1.2869 | 0.8299 | 0.8263 | 0.8258 | 0.8263 |
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+ | 0.0002 | 5.27 | 1170 | 1.2915 | 0.8349 | 0.8319 | 0.8311 | 0.8319 |
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+ | 0.0007 | 5.41 | 1200 | 1.2616 | 0.8265 | 0.8235 | 0.8228 | 0.8235 |
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+ | 0.0003 | 5.54 | 1230 | 1.2504 | 0.8272 | 0.8235 | 0.8231 | 0.8235 |
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+ | 0.0012 | 5.68 | 1260 | 1.3068 | 0.8290 | 0.8263 | 0.8258 | 0.8263 |
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+ | 0.0002 | 5.81 | 1290 | 1.3276 | 0.8322 | 0.8291 | 0.8285 | 0.8291 |
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+ | 0.0002 | 5.95 | 1320 | 1.3319 | 0.8294 | 0.8263 | 0.8261 | 0.8263 |
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+ | 0.0055 | 6.08 | 1350 | 1.3286 | 0.8243 | 0.8207 | 0.8206 | 0.8207 |
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+ | 0.0002 | 6.22 | 1380 | 1.3927 | 0.8326 | 0.8291 | 0.8291 | 0.8291 |
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+ | 0.0143 | 6.35 | 1410 | 1.3880 | 0.8180 | 0.8151 | 0.8149 | 0.8151 |
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+ | 0.0002 | 6.49 | 1440 | 1.3890 | 0.8211 | 0.8179 | 0.8179 | 0.8179 |
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+ | 0.0002 | 6.62 | 1470 | 1.3921 | 0.8211 | 0.8179 | 0.8179 | 0.8179 |
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+ | 0.0003 | 6.76 | 1500 | 1.4093 | 0.8148 | 0.8095 | 0.8095 | 0.8095 |
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+ | 0.0002 | 6.89 | 1530 | 1.4409 | 0.8183 | 0.8095 | 0.8097 | 0.8095 |
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+ | 0.0196 | 7.03 | 1560 | 1.4491 | 0.8179 | 0.8095 | 0.8091 | 0.8095 |
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+ | 0.0082 | 7.16 | 1590 | 1.5177 | 0.8067 | 0.8011 | 0.8003 | 0.8011 |
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+ | 0.0001 | 7.3 | 1620 | 1.5283 | 0.8064 | 0.8011 | 0.8004 | 0.8011 |
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+ | 0.0007 | 7.43 | 1650 | 1.4816 | 0.8072 | 0.8039 | 0.8032 | 0.8039 |
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+ | 0.0001 | 7.57 | 1680 | 1.4737 | 0.8072 | 0.8039 | 0.8032 | 0.8039 |
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+ | 0.0002 | 7.7 | 1710 | 1.4429 | 0.8111 | 0.8095 | 0.8090 | 0.8095 |
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+ | 0.0005 | 7.84 | 1740 | 1.4328 | 0.8087 | 0.8067 | 0.8064 | 0.8067 |
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+ | 0.0001 | 7.97 | 1770 | 1.4271 | 0.8088 | 0.8067 | 0.8065 | 0.8067 |
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+ | 0.0001 | 8.11 | 1800 | 1.4281 | 0.8088 | 0.8067 | 0.8065 | 0.8067 |
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+ | 0.0001 | 8.24 | 1830 | 1.4296 | 0.8115 | 0.8095 | 0.8092 | 0.8095 |
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+ | 0.0002 | 8.38 | 1860 | 1.4354 | 0.8260 | 0.8235 | 0.8234 | 0.8235 |
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+ | 0.0001 | 8.51 | 1890 | 1.4407 | 0.8260 | 0.8235 | 0.8234 | 0.8235 |
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+ | 0.0001 | 8.65 | 1920 | 1.4333 | 0.8176 | 0.8151 | 0.8148 | 0.8151 |
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+ | 0.0001 | 8.78 | 1950 | 1.4341 | 0.8176 | 0.8151 | 0.8148 | 0.8151 |
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+ | 0.0001 | 8.92 | 1980 | 1.4357 | 0.8176 | 0.8151 | 0.8148 | 0.8151 |
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+ | 0.0001 | 9.05 | 2010 | 1.4371 | 0.8176 | 0.8151 | 0.8148 | 0.8151 |
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+ | 0.0009 | 9.19 | 2040 | 1.4660 | 0.8278 | 0.8235 | 0.8236 | 0.8235 |
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+ | 0.0128 | 9.32 | 2070 | 1.4876 | 0.8278 | 0.8235 | 0.8236 | 0.8235 |
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+ | 0.0001 | 9.46 | 2100 | 1.4889 | 0.8278 | 0.8235 | 0.8236 | 0.8235 |
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+ | 0.0001 | 9.59 | 2130 | 1.4849 | 0.8278 | 0.8235 | 0.8236 | 0.8235 |
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+ | 0.0001 | 9.73 | 2160 | 1.4847 | 0.8278 | 0.8235 | 0.8236 | 0.8235 |
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+ | 0.0001 | 9.86 | 2190 | 1.4851 | 0.8278 | 0.8235 | 0.8236 | 0.8235 |
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+ | 0.0001 | 10.0 | 2220 | 1.4852 | 0.8278 | 0.8235 | 0.8236 | 0.8235 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.3
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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+ "id2label": {
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+ "0": "anger",
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+ "1": "fear",
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+ "2": "joy",
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+ "3": "love",
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+ "4": "neutral",
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+ "5": "sadness",
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+ "problem_type": "single_label_classification",
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+ "transformers_version": "4.39.3",
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