gnurt2041
/

roberta-hate-speech-dynabench-r4-target-tuned

@@ -1,199 +1,79 @@
 ---
 library_name: transformers
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 ---
 library_name: transformers
+base_model: facebook/roberta-hate-speech-dynabench-r4-target
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: results
+  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. -->
+# results
+This model is a fine-tuned version of [facebook/roberta-hate-speech-dynabench-r4-target](https://huggingface.co/facebook/roberta-hate-speech-dynabench-r4-target) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1543
+- Accuracy: 0.975
+- Precision: 0.9761
+- Recall: 0.975
+- F1: 0.9750
+## 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: 3e-05
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 5
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.4904        | 0.9895 | 59   | 0.3492          | 0.9      | 0.9015    | 0.9    | 0.8997 |
+| 0.1344        | 1.9958 | 119  | 0.3267          | 0.9333   | 0.9374    | 0.9333 | 0.9330 |
+| 0.0614        | 2.9853 | 178  | 0.2695          | 0.9333   | 0.9339    | 0.9333 | 0.9334 |
+| 0.041         | 3.9916 | 238  | 0.2203          | 0.9583   | 0.9614    | 0.9583 | 0.9582 |
+| 0.0674        | 4.9979 | 298  | 0.2079          | 0.9667   | 0.9687    | 0.9667 | 0.9666 |
+| 0.0006        | 5.9874 | 357  | 0.1543          | 0.975    | 0.9761    | 0.975  | 0.9750 |
+| 0.0004        | 6.9937 | 417  | 0.1883          | 0.975    | 0.9751    | 0.975  | 0.9750 |
+| 0.0002        | 8.0    | 477  | 0.1628          | 0.9667   | 0.9667    | 0.9667 | 0.9667 |
+| 0.0001        | 8.9895 | 536  | 0.2980          | 0.9667   | 0.9687    | 0.9667 | 0.9666 |
+| 0.0001        | 9.8952 | 590  | 0.2377          | 0.975    | 0.9761    | 0.975  | 0.9750 |
+### Framework versions
+- Transformers 4.44.2
+- Pytorch 2.5.0+cu121
+- Datasets 3.0.2
+- Tokenizers 0.19.1