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: xlmr-large-nli-indoindo
+  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. -->
+
+# xlmr-large-nli-indoindo
+
+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: 1.3131
+- Accuracy: 0.8584
+- Precision: 0.8584
+- Recall: 0.8584
+- F1 Score: 0.8585
+
+## 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: 3e-06
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 6
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1 Score |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:|
+| 1.449         | 1.0   | 10330 | 1.2228          | 0.7838   | 0.7838    | 0.7838 | 0.7810   |
+| 1.2575        | 2.0   | 20660 | 1.1182          | 0.8257   | 0.8257    | 0.8257 | 0.8273   |
+| 0.8123        | 3.0   | 30990 | 1.1538          | 0.8489   | 0.8489    | 0.8489 | 0.8488   |
+| 0.6541        | 4.0   | 41320 | 1.1288          | 0.8562   | 0.8562    | 0.8562 | 0.8558   |
+| 0.3653        | 5.0   | 51650 | 1.2424          | 0.8543   | 0.8543    | 0.8543 | 0.8544   |
+| 0.3436        | 6.0   | 61980 | 1.3131          | 0.8584   | 0.8584    | 0.8584 | 0.8585   |
+
+
+### Framework versions
+
+- Transformers 4.33.0
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.13.3

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": "4.33.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

pytorch_model.bin

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training_args.bin

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