End of training

README.md

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+---
+license: mit
+base_model: FacebookAI/xlm-roberta-large
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: xlm-roberta-large-finetuned-ner-harem
+  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-roberta-large-finetuned-ner-harem
+
+This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1622
+- Precision: 0.8344
+- Recall: 0.8412
+- F1: 0.8378
+- Accuracy: 0.9745
+
+## 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: 4
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 32
+- 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 | Precision | Recall | F1     | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 0.9938 | 140  | 0.1806          | 0.6310    | 0.6557 | 0.6431 | 0.9533   |
+| No log        | 1.9947 | 281  | 0.1334          | 0.7314    | 0.7691 | 0.7497 | 0.9642   |
+| No log        | 2.9956 | 422  | 0.1332          | 0.7751    | 0.8103 | 0.7923 | 0.9712   |
+| 0.2049        | 3.9965 | 563  | 0.1133          | 0.7948    | 0.8144 | 0.8045 | 0.9706   |
+| 0.2049        | 4.9973 | 704  | 0.1215          | 0.814     | 0.8392 | 0.8264 | 0.9748   |
+| 0.2049        | 5.9982 | 845  | 0.1274          | 0.8097    | 0.8247 | 0.8172 | 0.9726   |
+| 0.2049        | 6.9991 | 986  | 0.1725          | 0.8079    | 0.8062 | 0.8070 | 0.9687   |
+| 0.0307        | 8.0    | 1127 | 0.1647          | 0.8396    | 0.8309 | 0.8352 | 0.9736   |
+| 0.0307        | 8.9938 | 1267 | 0.1678          | 0.8420    | 0.8351 | 0.8385 | 0.9726   |
+| 0.0307        | 9.9379 | 1400 | 0.1622          | 0.8344    | 0.8412 | 0.8378 | 0.9745   |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.1.2
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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