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---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-NER-ind
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-base-NER-ind
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: 0.1404
- F1: 0.8130
## Model description
Model is trained specifically for indian context, we used sentence-piece tokenizer to train the model, so use the sentences with proper delimeter like(. , ?) and appropiate capitalization of words.
## 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: 3
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| No log | 1.0 | 2509 | 0.1427 | 0.7972 |
| No log | 2.0 | 5019 | 0.1366 | 0.8101 |
| 0.1384 | 3.0 | 7529 | 0.1366 | 0.8139 |
| 0.1384 | 4.0 | 10036 | 0.1404 | 0.8130 |
### Framework versions
- Transformers 4.27.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|