Add trained model

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+library_name: transformers
+license: mit
+base_model: adity12345/RoBerta_covi19_rumor
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: Roberta_feverous
+  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. -->
+
+# Roberta_feverous
+
+This model is a fine-tuned version of [adity12345/RoBerta_covi19_rumor](https://huggingface.co/adity12345/RoBerta_covi19_rumor) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6168
+- Accuracy: 0.674
+- Auc: 0.67
+- Precision: 0.677
+- Recall: 0.897
+- F1: 0.771
+- F1-macro: 0.6
+- F1-micro: 0.674
+- F1-weighted: 0.639
+
+## 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
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Use OptimizerNames.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: 2
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Auc   | Precision | Recall | F1    | F1-macro | F1-micro | F1-weighted |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:-----:|:---------:|:------:|:-----:|:--------:|:--------:|:-----------:|
+| 0.6385        | 0.2896 | 500  | 0.6240          | 0.666    | 0.646 | 0.656     | 0.96   | 0.779 | 0.546    | 0.666    | 0.599       |
+| 0.6294        | 0.5793 | 1000 | 0.6270          | 0.665    | 0.652 | 0.673     | 0.885  | 0.764 | 0.593    | 0.665    | 0.632       |
+| 0.627         | 0.8689 | 1500 | 0.6192          | 0.669    | 0.658 | 0.674     | 0.891  | 0.768 | 0.595    | 0.669    | 0.634       |
+| 0.6126        | 1.1581 | 2000 | 0.6185          | 0.674    | 0.662 | 0.665     | 0.945  | 0.781 | 0.573    | 0.674    | 0.621       |
+| 0.6044        | 1.4478 | 2500 | 0.6155          | 0.673    | 0.665 | 0.669     | 0.927  | 0.777 | 0.582    | 0.673    | 0.627       |
+| 0.5942        | 1.7374 | 3000 | 0.6168          | 0.674    | 0.67  | 0.677     | 0.897  | 0.771 | 0.6      | 0.674    | 0.639       |
+
+
+### Framework versions
+
+- Transformers 4.55.2
+- Pytorch 2.8.0+cu126
+- Datasets 4.0.0
+- Tokenizers 0.21.4

config.json

@@ -0,0 +1,36 @@
+{
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "fake",
+    "1": "real"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "fake": 0,
+    "real": 1
+  },
+  "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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

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sentencepiece.bpe.model

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special_tokens_map.json

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tokenizer.json

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tokenizer_config.json

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+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
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+  "padding_side": "right",
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+  "tokenizer_class": "XLMRobertaTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}

training_args.bin

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