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README.md

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+---
+license: mit
+base_model: xlm-roberta-base
+tags:
+- generated_from_trainer
+datasets:
+- fjd_dataset
+model-index:
+- name: xlmr-lstm-crf-resume-ner4
+  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-lstm-crf-resume-ner4
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the fjd_dataset dataset.
+It achieves the following results on the evaluation set:
+- eval_loss: 0.1764
+- eval_precision: 0.5811
+- eval_recall: 0.5602
+- eval_f1: 0.5705
+- eval_accuracy: 0.9501
+- eval_runtime: 52.6822
+- eval_samples_per_second: 94.415
+- eval_steps_per_second: 2.961
+- epoch: 4.0
+- step: 3680
+
+## 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: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 100
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.1

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