Model save

README.md

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+---
+license: apache-2.0
+base_model: BSC-LT/roberta-base-bne
+tags:
+- generated_from_trainer
+model-index:
+- name: fine-tunning-roberta-bne-hate-offensive
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# fine-tunning-roberta-bne-hate-offensive
+
+This model is a fine-tuned version of [BSC-LT/roberta-base-bne](https://huggingface.co/BSC-LT/roberta-base-bne) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6211
+- Hate/offensive F1: 0.8958
+- Hate/offensive Precision: 0.9421
+- Hate/offensive Recall: 0.8539
+- Normal F1: 0.9519
+- Normal Precision: 0.9308
+- Normal Recall: 0.9740
+- Micro F1: 0.9342
+- Acc: 0.9342
+- Macro F1: 0.9239
+- Macro Precision: 0.9364
+- Macro Recall: 0.9139
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Hate/offensive F1 | Hate/offensive Precision | Hate/offensive Recall | Normal F1 | Normal Precision | Normal Recall | Micro F1 | Acc    | Macro F1 | Macro Precision | Macro Recall |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:------------------------:|:---------------------:|:---------:|:----------------:|:-------------:|:--------:|:------:|:--------:|:---------------:|:------------:|
+| 0.2865        | 1.0   | 1064 | 0.3398          | 0.8953            | 0.9324                   | 0.8610                | 0.9510    | 0.9336           | 0.9691        | 0.9332   | 0.9332 | 0.9231   | 0.9330          | 0.9150       |
+| 0.1527        | 2.0   | 2128 | 0.3417          | 0.8942            | 0.9204                   | 0.8695                | 0.9497    | 0.9370           | 0.9627        | 0.9318   | 0.9318 | 0.9220   | 0.9287          | 0.9161       |
+| 0.047         | 3.0   | 3192 | 0.5626          | 0.8914            | 0.9668                   | 0.8270                | 0.9518    | 0.9199           | 0.9859        | 0.9332   | 0.9332 | 0.9216   | 0.9434          | 0.9064       |
+| 0.0145        | 4.0   | 4256 | 0.5874          | 0.8986            | 0.9396                   | 0.8610                | 0.9528    | 0.9338           | 0.9726        | 0.9356   | 0.9356 | 0.9257   | 0.9367          | 0.9168       |
+| 0.0029        | 5.0   | 5320 | 0.6211          | 0.8958            | 0.9421                   | 0.8539                | 0.9519    | 0.9308           | 0.9740        | 0.9342   | 0.9342 | 0.9239   | 0.9364          | 0.9139       |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0

model.safetensors

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