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news-category-classification

Text Classification
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distilbert
Generated from Trainer
text-embeddings-inference
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category-classification

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  1. README.md +132 -0
  2. config.json +112 -0
  3. model.safetensors +3 -0
  4. runs/Jul15_17-00-24_9252199e1b58/events.out.tfevents.1752598831.9252199e1b58.1329.0 +3 -0
  5. runs/Jul15_17-01-42_9252199e1b58/events.out.tfevents.1752598906.9252199e1b58.1329.1 +3 -0
  6. runs/Jul15_17-02-08_9252199e1b58/events.out.tfevents.1752599003.9252199e1b58.1329.2 +3 -0
  7. runs/Jul15_17-05-20_9252199e1b58/events.out.tfevents.1752599126.9252199e1b58.1329.3 +3 -0
  8. special_tokens_map.json +7 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +56 -0
  11. training_args.bin +3 -0
  12. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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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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+ - accuracy
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+ - f1
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+ model-index:
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+ - name: results
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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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+ # results
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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: 1.0016
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+ - Accuracy: 0.7135
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+ - F1: 0.7084
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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: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 3
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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+ |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
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+ | 1.2147 | 0.0424 | 500 | 1.4753 | 0.6069 | 0.5742 |
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+ | 1.024 | 0.0848 | 1000 | 1.4624 | 0.6169 | 0.5880 |
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+ | 1.3489 | 0.1273 | 1500 | 1.3591 | 0.6292 | 0.5975 |
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+ | 1.3487 | 0.1697 | 2000 | 1.2964 | 0.6416 | 0.6179 |
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+ | 1.2584 | 0.2121 | 2500 | 1.2626 | 0.6419 | 0.6290 |
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+ | 1.2656 | 0.2545 | 3000 | 1.2225 | 0.6556 | 0.6334 |
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+ | 1.2501 | 0.2970 | 3500 | 1.1955 | 0.6550 | 0.6344 |
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+ | 1.1692 | 0.3394 | 4000 | 1.1675 | 0.6656 | 0.6518 |
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+ | 1.1625 | 0.3818 | 4500 | 1.1735 | 0.6612 | 0.6471 |
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+ | 1.2122 | 0.4242 | 5000 | 1.1384 | 0.6718 | 0.6566 |
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+ | 1.1813 | 0.4667 | 5500 | 1.1344 | 0.6720 | 0.6572 |
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+ | 1.1571 | 0.5091 | 6000 | 1.1228 | 0.6763 | 0.6666 |
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+ | 1.1468 | 0.5515 | 6500 | 1.1067 | 0.6728 | 0.6671 |
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+ | 1.1663 | 0.5939 | 7000 | 1.0877 | 0.6800 | 0.6716 |
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+ | 1.0567 | 0.6363 | 7500 | 1.0971 | 0.6798 | 0.6725 |
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+ | 1.0834 | 0.6788 | 8000 | 1.0802 | 0.6863 | 0.6745 |
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+ | 1.1045 | 0.7212 | 8500 | 1.0645 | 0.6871 | 0.6753 |
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+ | 1.0942 | 0.7636 | 9000 | 1.0495 | 0.6936 | 0.6827 |
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+ | 1.0286 | 0.8060 | 9500 | 1.0579 | 0.6909 | 0.6766 |
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+ | 1.0633 | 0.8485 | 10000 | 1.0628 | 0.6845 | 0.6764 |
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+ | 1.0718 | 0.8909 | 10500 | 1.0430 | 0.6944 | 0.6858 |
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+ | 1.0848 | 0.9333 | 11000 | 1.0288 | 0.6933 | 0.6870 |
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+ | 1.0124 | 0.9757 | 11500 | 1.0291 | 0.6946 | 0.6884 |
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+ | 0.8907 | 1.0182 | 12000 | 1.0314 | 0.6945 | 0.6878 |
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+ | 0.8527 | 1.0606 | 12500 | 1.0173 | 0.7021 | 0.6952 |
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+ | 0.79 | 1.1030 | 13000 | 1.0402 | 0.6960 | 0.6866 |
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+ | 0.8419 | 1.1454 | 13500 | 1.0281 | 0.7004 | 0.6925 |
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+ | 0.8665 | 1.1878 | 14000 | 1.0244 | 0.7003 | 0.6938 |
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+ | 0.8793 | 1.2303 | 14500 | 1.0221 | 0.7008 | 0.6930 |
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+ | 0.8335 | 1.2727 | 15000 | 1.0097 | 0.7012 | 0.6955 |
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+ | 0.8149 | 1.3151 | 15500 | 1.0163 | 0.7019 | 0.6955 |
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+ | 0.8193 | 1.3575 | 16000 | 1.0248 | 0.7006 | 0.6939 |
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+ | 0.8453 | 1.4000 | 16500 | 1.0151 | 0.7025 | 0.6956 |
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+ | 0.8591 | 1.4424 | 17000 | 1.0110 | 0.7043 | 0.6945 |
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+ | 0.8581 | 1.4848 | 17500 | 1.0132 | 0.7050 | 0.6958 |
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+ | 0.9052 | 1.5272 | 18000 | 1.0104 | 0.7036 | 0.6981 |
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+ | 0.8667 | 1.5697 | 18500 | 1.0080 | 0.7057 | 0.6970 |
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+ | 0.8016 | 1.6121 | 19000 | 1.0098 | 0.7012 | 0.6963 |
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+ | 0.8507 | 1.6545 | 19500 | 1.0061 | 0.7044 | 0.6975 |
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+ | 0.8037 | 1.6969 | 20000 | 1.0095 | 0.7069 | 0.6985 |
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+ | 0.8371 | 1.7394 | 20500 | 1.0007 | 0.7077 | 0.6980 |
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+ | 0.7558 | 1.7818 | 21000 | 0.9975 | 0.7035 | 0.6985 |
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+ | 0.7919 | 1.8242 | 21500 | 0.9937 | 0.7077 | 0.6998 |
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+ | 0.8059 | 1.8666 | 22000 | 0.9900 | 0.7097 | 0.7037 |
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+ | 0.799 | 1.9090 | 22500 | 0.9918 | 0.7112 | 0.7054 |
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+ | 0.8072 | 1.9515 | 23000 | 0.9875 | 0.7098 | 0.7020 |
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+ | 0.8052 | 1.9939 | 23500 | 0.9902 | 0.7088 | 0.7017 |
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+ | 0.6761 | 2.0363 | 24000 | 1.0025 | 0.7079 | 0.7009 |
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+ | 0.7107 | 2.0787 | 24500 | 1.0087 | 0.7108 | 0.7053 |
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+ | 0.667 | 2.1212 | 25000 | 1.0080 | 0.7090 | 0.7042 |
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+ | 0.6489 | 2.1636 | 25500 | 1.0024 | 0.7089 | 0.7035 |
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+ | 0.6945 | 2.2060 | 26000 | 1.0097 | 0.7107 | 0.7039 |
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+ | 0.6609 | 2.2484 | 26500 | 1.0089 | 0.7092 | 0.7036 |
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+ | 0.6442 | 2.2909 | 27000 | 1.0178 | 0.7113 | 0.7037 |
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+ | 0.6822 | 2.3333 | 27500 | 1.0124 | 0.7099 | 0.7048 |
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+ | 0.6677 | 2.3757 | 28000 | 1.0089 | 0.7089 | 0.7034 |
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+ | 0.6272 | 2.4181 | 28500 | 1.0051 | 0.7114 | 0.7062 |
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+ | 0.6336 | 2.4605 | 29000 | 1.0110 | 0.7121 | 0.7075 |
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+ | 0.6247 | 2.5030 | 29500 | 1.0089 | 0.7106 | 0.7056 |
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+ | 0.6635 | 2.5454 | 30000 | 1.0112 | 0.7131 | 0.7077 |
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+ | 0.6401 | 2.5878 | 30500 | 1.0092 | 0.7127 | 0.7076 |
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+ | 0.6488 | 2.6302 | 31000 | 1.0081 | 0.7115 | 0.7062 |
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+ | 0.64 | 2.6727 | 31500 | 1.0066 | 0.7124 | 0.7077 |
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+ | 0.6764 | 2.7151 | 32000 | 1.0050 | 0.7123 | 0.7077 |
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+ | 0.6554 | 2.7575 | 32500 | 1.0062 | 0.7124 | 0.7070 |
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+ | 0.6239 | 2.7999 | 33000 | 1.0055 | 0.7128 | 0.7074 |
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+ | 0.669 | 2.8424 | 33500 | 1.0045 | 0.7129 | 0.7076 |
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+ | 0.6742 | 2.8848 | 34000 | 1.0019 | 0.7138 | 0.7084 |
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+ | 0.5769 | 2.9272 | 34500 | 1.0017 | 0.7136 | 0.7086 |
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+ | 0.6783 | 2.9696 | 35000 | 1.0016 | 0.7135 | 0.7084 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.53.1
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+ - Pytorch 2.6.0+cu124
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+ - Datasets 2.14.4
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+ - Tokenizers 0.21.2
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