MultiPRIDE-LGBT-Pretrain-es

README.md

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+---
+library_name: transformers
+base_model: cardiffnlp/twitter-xlm-roberta-base-hate-spanish
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: MultiPRIDE-LGBT-Pretrain-es
+  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. -->
+
+# MultiPRIDE-LGBT-Pretrain-es
+
+This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-hate-spanish](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-hate-spanish) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7451
+- Accuracy: 0.8182
+- F1: 0.8481
+- Precision: 0.8375
+- Recall: 0.8590
+
+## 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: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.7172        | 1.0   | 77   | 0.5342          | 0.7576   | 0.8118 | 0.75      | 0.8846 |
+| 0.53          | 2.0   | 154  | 0.5837          | 0.7652   | 0.8268 | 0.7327    | 0.9487 |
+| 0.5941        | 3.0   | 231  | 0.7575          | 0.6742   | 0.7817 | 0.6471    | 0.9872 |
+| 0.4301        | 4.0   | 308  | 0.5676          | 0.7879   | 0.8272 | 0.7976    | 0.8590 |
+| 0.4044        | 5.0   | 385  | 0.4593          | 0.8333   | 0.8553 | 0.8784    | 0.8333 |
+| 0.2799        | 6.0   | 462  | 0.6975          | 0.7879   | 0.8056 | 0.8788    | 0.7436 |
+| 0.1835        | 7.0   | 539  | 0.8903          | 0.8030   | 0.8415 | 0.8023    | 0.8846 |
+| 0.1452        | 8.0   | 616  | 0.7451          | 0.8182   | 0.8481 | 0.8375    | 0.8590 |
+
+
+### Framework versions
+
+- Transformers 4.57.3
+- Pytorch 2.9.1+cu128
+- Datasets 4.4.1
+- Tokenizers 0.22.1

config.json

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+{
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "NOT-HATE",
+    "1": "HATE"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "HATE": 1,
+    "NOT-HATE": 0
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "transformers_version": "4.57.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

model.safetensors

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training_args.bin

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