End of training

README.md

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+---
+license: mit
+base_model: xlm-roberta-large
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+model-index:
+- name: fine-tuning-xlmr-large
+  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. -->
+
+# fine-tuning-xlmr-large
+
+This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.7558
+- Accuracy: 0.7692
+- Precision: 0.7692
+- Recall: 0.7692
+- F1 Score: 0.7693
+
+## 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: 1e-06
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 101
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step   | Validation Loss | Accuracy | Precision | Recall | F1 Score |
+|:-------------:|:-----:|:------:|:---------------:|:--------:|:---------:|:------:|:--------:|
+| 1.3385        | 1.0   | 10330  | 1.8072          | 0.5708   | 0.5708    | 0.5708 | 0.5622   |
+| 1.7231        | 2.0   | 20660  | 1.8354          | 0.6445   | 0.6445    | 0.6445 | 0.6454   |
+| 1.4049        | 3.0   | 30990  | 1.8380          | 0.6969   | 0.6969    | 0.6969 | 0.6990   |
+| 1.4543        | 4.0   | 41320  | 1.5726          | 0.7415   | 0.7415    | 0.7415 | 0.7417   |
+| 1.4139        | 5.0   | 51650  | 1.6838          | 0.7424   | 0.7424    | 0.7424 | 0.7439   |
+| 1.2368        | 6.0   | 61980  | 1.6794          | 0.7424   | 0.7424    | 0.7424 | 0.7448   |
+| 1.0418        | 7.0   | 72310  | 1.6720          | 0.7542   | 0.7542    | 0.7542 | 0.7556   |
+| 1.246         | 8.0   | 82640  | 1.6746          | 0.7638   | 0.7638    | 0.7638 | 0.7642   |
+| 0.9896        | 9.0   | 92970  | 1.7497          | 0.7674   | 0.7674    | 0.7674 | 0.7666   |
+| 0.9855        | 10.0  | 103300 | 1.7558          | 0.7692   | 0.7692    | 0.7692 | 0.7693   |
+
+
+### Framework versions
+
+- Transformers 4.36.0.dev0
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.15.0

config.json

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+{
+  "_name_or_path": "xlm-roberta-large",
+  "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": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "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": "4.36.0.dev0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

model.safetensors

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training_args.bin

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