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---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- uner_ser_set
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: uner_ser_set
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: uner_ser_set
type: uner_ser_set
config: default
split: validation
args: default
metrics:
- name: Precision
type: precision
value: 0.9338624338624338
- name: Recall
type: recall
value: 0.9489247311827957
- name: F1
type: f1
value: 0.9413333333333335
- name: Accuracy
type: accuracy
value: 0.9930792962561494
---
<!-- 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. -->
# uner_ser_set
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the uner_ser_set dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0440
- Precision: 0.9339
- Recall: 0.9489
- F1: 0.9413
- Accuracy: 0.9931
## 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-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
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 1.10.1+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
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