Model save

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.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

@@ -0,0 +1,74 @@
+---
+library_name: transformers
+license: mit
+base_model: BAAI/bge-m3
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: fosh-detector-v2-bge
+  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. -->
+
+# fosh-detector-v2-bge
+
+This model is a fine-tuned version of [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0486
+- Accuracy: 0.9926
+- Precision: 0.9919
+- Recall: 0.9919
+- F1: 0.9919
+
+## 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: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.335         | 0.125 | 50   | 0.2109          | 0.9296   | 0.9333    | 0.9106 | 0.9218 |
+| 0.1921        | 0.25  | 100  | 0.0963          | 0.9741   | 0.9603    | 0.9837 | 0.9719 |
+| 0.1344        | 0.375 | 150  | 0.1128          | 0.9741   | 0.9833    | 0.9593 | 0.9712 |
+| 0.1141        | 0.5   | 200  | 0.0857          | 0.9815   | 0.9758    | 0.9837 | 0.9798 |
+| 0.1016        | 0.625 | 250  | 0.0780          | 0.9815   | 0.9758    | 0.9837 | 0.9798 |
+| 0.0832        | 0.75  | 300  | 0.0895          | 0.9778   | 0.9916    | 0.9593 | 0.9752 |
+| 0.0899        | 0.875 | 350  | 0.0480          | 0.9926   | 0.9919    | 0.9919 | 0.9919 |
+| 0.0787        | 1.0   | 400  | 0.0486          | 0.9926   | 0.9919    | 0.9919 | 0.9919 |
+
+
+### Framework versions
+
+- Transformers 4.50.3
+- Pytorch 2.6.0+cu124
+- Datasets 3.3.2
+- Tokenizers 0.21.1

config.json

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+    "XLMRobertaForSequenceClassification"
+  ],
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+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.50.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

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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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+}

training_args.bin

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