End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: intfloat/multilingual-e5-large
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: intfloat-multilingual-e5-large-english-fp16
+  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. -->
+
+# intfloat-multilingual-e5-large-english-fp16
+
+This model is a fine-tuned version of [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3003
+- Accuracy: 0.8851
+- Precision: 0.8857
+- Recall: 0.8851
+- F1: 0.8835
+
+## 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
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 128
+- 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
+- lr_scheduler_warmup_ratio: 0.3
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 1.0422        | 0.3922 | 50   | 0.8866          | 0.6081   | 0.6817    | 0.6081 | 0.5308 |
+| 0.703         | 0.7843 | 100  | 0.4687          | 0.8266   | 0.8265    | 0.8266 | 0.8242 |
+| 0.3701        | 1.1725 | 150  | 0.3189          | 0.8787   | 0.8783    | 0.8787 | 0.8784 |
+| 0.3018        | 1.5647 | 200  | 0.3163          | 0.8861   | 0.8855    | 0.8861 | 0.8852 |
+| 0.2988        | 1.9569 | 250  | 0.3223          | 0.8797   | 0.8828    | 0.8797 | 0.8770 |
+| 0.2324        | 2.3451 | 300  | 0.3003          | 0.8851   | 0.8857    | 0.8851 | 0.8835 |
+| 0.2337        | 2.7373 | 350  | 0.2901          | 0.8905   | 0.8909    | 0.8905 | 0.8904 |
+| 0.2066        | 3.1255 | 400  | 0.3938          | 0.8851   | 0.8866    | 0.8851 | 0.8831 |
+| 0.1711        | 3.5176 | 450  | 0.3373          | 0.8905   | 0.8910    | 0.8905 | 0.8907 |
+| 0.1668        | 3.9098 | 500  | 0.3527          | 0.8861   | 0.8859    | 0.8861 | 0.8848 |
+
+
+### Framework versions
+
+- Transformers 4.51.1
+- Pytorch 2.6.0+cu124
+- Datasets 3.5.0
+- Tokenizers 0.21.1

config.json

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+{
+  "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": 1024,
+  "id2label": {
+    "0": "negative",
+    "1": "positive",
+    "2": "neutral"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "negative": 0,
+    "neutral": 2,
+    "positive": 1
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "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": "4.51.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

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training_args.bin

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