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visobert_v1

Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
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  1. README.md +86 -0
  2. classification_report_test.txt +14 -0
  3. confusion_matrix_test.csv +4 -0
  4. model.safetensors +1 -1
README.md ADDED
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+ ---
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+ library_name: transformers
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+ base_model: uitnlp/visobert
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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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+ model-index:
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+ - name: visobert_v1
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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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+ # visobert_v1
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+
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+ This model is a fine-tuned version of [uitnlp/visobert](https://huggingface.co/uitnlp/visobert) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4766
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+ - Accuracy: 0.9337
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+ - Precision Macro: 0.8527
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+ - Recall Macro: 0.8055
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+ - F1 Macro: 0.8251
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+ - F1 Weighted: 0.9316
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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: 3e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 128
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+ - optimizer: Use 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: 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 | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 Weighted |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:-----------:|
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+ | 0.377 | 1.0 | 90 | 0.2037 | 0.9406 | 0.9012 | 0.7694 | 0.8068 | 0.9350 |
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+ | 0.1659 | 2.0 | 180 | 0.2094 | 0.9356 | 0.8396 | 0.8232 | 0.8309 | 0.9348 |
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+ | 0.0966 | 3.0 | 270 | 0.2278 | 0.9381 | 0.8463 | 0.8165 | 0.8298 | 0.9367 |
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+ | 0.0696 | 4.0 | 360 | 0.2619 | 0.9318 | 0.8438 | 0.7756 | 0.8003 | 0.9280 |
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+ | 0.0468 | 5.0 | 450 | 0.3120 | 0.9324 | 0.8362 | 0.8128 | 0.8234 | 0.9313 |
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+ | 0.0337 | 6.0 | 540 | 0.3576 | 0.9311 | 0.8376 | 0.7912 | 0.8103 | 0.9287 |
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+ | 0.0244 | 7.0 | 630 | 0.3796 | 0.9292 | 0.8428 | 0.7816 | 0.8051 | 0.9261 |
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+ | 0.019 | 8.0 | 720 | 0.4309 | 0.9349 | 0.8612 | 0.8070 | 0.8286 | 0.9327 |
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+ | 0.0094 | 9.0 | 810 | 0.4022 | 0.9337 | 0.8565 | 0.8134 | 0.8318 | 0.9319 |
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+ | 0.0098 | 10.0 | 900 | 0.4181 | 0.9349 | 0.8534 | 0.8062 | 0.8259 | 0.9329 |
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+ | 0.0039 | 11.0 | 990 | 0.4484 | 0.9330 | 0.8542 | 0.8091 | 0.8281 | 0.9311 |
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+ | 0.0028 | 12.0 | 1080 | 0.4580 | 0.9349 | 0.8554 | 0.8106 | 0.8294 | 0.9330 |
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+ | 0.0028 | 13.0 | 1170 | 0.4554 | 0.9318 | 0.8613 | 0.7998 | 0.8242 | 0.9292 |
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+ | 0.0031 | 14.0 | 1260 | 0.4575 | 0.9330 | 0.8579 | 0.8009 | 0.8237 | 0.9306 |
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+ | 0.0018 | 15.0 | 1350 | 0.4547 | 0.9356 | 0.8617 | 0.8068 | 0.8291 | 0.9333 |
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+ | 0.0004 | 16.0 | 1440 | 0.4631 | 0.9343 | 0.8455 | 0.8182 | 0.8305 | 0.9331 |
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+ | 0.0006 | 17.0 | 1530 | 0.4642 | 0.9356 | 0.8542 | 0.8152 | 0.8319 | 0.9339 |
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+ | 0.0008 | 18.0 | 1620 | 0.4736 | 0.9343 | 0.8534 | 0.8141 | 0.8311 | 0.9326 |
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+ | 0.0014 | 19.0 | 1710 | 0.4753 | 0.9337 | 0.8527 | 0.8055 | 0.8251 | 0.9316 |
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+ | 0.0009 | 20.0 | 1800 | 0.4766 | 0.9337 | 0.8527 | 0.8055 | 0.8251 | 0.9316 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.55.0
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+ - Pytorch 2.7.0+cu126
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+ - Datasets 4.0.0
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+ - Tokenizers 0.21.4
classification_report_test.txt ADDED
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+ precision recall f1-score support
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+
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+ negative 0.93 0.95 0.94 1409
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+ neutral 0.64 0.43 0.51 167
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+ positive 0.94 0.94 0.94 1590
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+
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+ accuracy 0.92 3166
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+ macro avg 0.83 0.77 0.80 3166
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+ weighted avg 0.92 0.92 0.92 3166
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+
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+ Confusion matrix:
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+ [[1345 22 42]
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+ [ 38 71 58]
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+ [ 70 18 1502]]
confusion_matrix_test.csv ADDED
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+ ,negative,neutral,positive
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+ negative,1345,22,42
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+ neutral,38,71,58
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+ positive,70,18,1502
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