End of training

.gitattributes

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 *.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 +1,72 @@
+---
+license: mit
+base_model: xlm-roberta-base
+tags:
+- generated_from_trainer
+model-index:
+- name: xlm_r_base-finetuned_after_mrp-v2-lucky-cloud-13
+  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. -->
+
+# xlm_r_base-finetuned_after_mrp-v2-lucky-cloud-13
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3887
+- Precision 0: 0.8520
+- Precision 1: 0.8061
+- Recall 0: 0.8721
+- Recall 1: 0.7783
+- F1 0: 0.8619
+- F1 1: 0.7920
+- Precision Weighted: 0.8334
+- Recall Weighted: 0.834
+- F1 Weighted: 0.8335
+- F1 Macro: 0.8269
+
+## 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: 16
+- eval_batch_size: 16
+- 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: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision 0 | Precision 1 | Recall 0 | Recall 1 | F1 0   | F1 1   | Precision Weighted | Recall Weighted | F1 Weighted | F1 Macro |
+|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-----------:|:--------:|:--------:|:------:|:------:|:------------------:|:---------------:|:-----------:|:--------:|
+| 0.521         | 1.0   | 469  | 0.4041          | 0.8088      | 0.8493      | 0.9172   | 0.6828   | 0.8596 | 0.7570 | 0.8252             | 0.822           | 0.8179      | 0.8083   |
+| 0.3826        | 2.0   | 938  | 0.3844          | 0.8719      | 0.7672      | 0.8296   | 0.8217   | 0.8502 | 0.7935 | 0.8294             | 0.8264          | 0.8272      | 0.8219   |
+| 0.3552        | 3.0   | 1407 | 0.3887          | 0.8520      | 0.8061      | 0.8721   | 0.7783   | 0.8619 | 0.7920 | 0.8334             | 0.834           | 0.8335      | 0.8269   |
+| 0.2801        | 4.0   | 1876 | 0.4242          | 0.8648      | 0.7843      | 0.8485   | 0.8059   | 0.8566 | 0.7949 | 0.8321             | 0.8312          | 0.8315      | 0.8258   |
+| 0.2223        | 5.0   | 2345 | 0.4754          | 0.8755      | 0.7726      | 0.8337   | 0.8266   | 0.8541 | 0.7987 | 0.8337             | 0.8308          | 0.8316      | 0.8264   |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "xlm-roberta-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,
+  "id2label": {
+    "0": "NOT",
+    "1": "OFF"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "NOT": 0,
+    "OFF": 1
+  },
+  "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": "4.41.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

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special_tokens_map.json

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tokenizer_config.json

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