End of training

README.md

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+---
+license: mit
+base_model: intfloat/multilingual-e5-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: multilingual-e5-base_censor_v0.1
+  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. -->
+
+# multilingual-e5-base_censor_v0.1
+
+This model is a fine-tuned version of [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5077
+- Accuracy: 0.7695
+- Precision: 0.7729
+- Recall: 0.7695
+- F1: 0.7708
+- Roc Auc: 0.8424
+- Per Class F1: [0.8104358705451601, 0.7061407261629732]
+
+## 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: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 3
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Roc Auc | Per Class F1                             |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|:----------------------------------------:|
+| 0.5485        | 1.0   | 2795 | 0.5249          | 0.7349   | 0.7498    | 0.7349 | 0.7383 | 0.8169  | [0.7723692804179954, 0.6827648980028912] |
+| 0.4871        | 2.0   | 5590 | 0.5059          | 0.7610   | 0.7667    | 0.7610 | 0.7629 | 0.8362  | [0.8013980033834657, 0.7000926419808541] |
+| 0.4392        | 3.0   | 8385 | 0.5077          | 0.7695   | 0.7729    | 0.7695 | 0.7708 | 0.8424  | [0.8104358705451601, 0.7061407261629732] |
+
+
+### Framework versions
+
+- Transformers 4.43.4
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "intfloat/multilingual-e5-base",
+  "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,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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": "4.43.4",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

model.safetensors

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+size 1112205008

training_args.bin

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