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---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
base_model: dslim/bert-large-NER
model-index:
- name: bert-finetuned-ner-adam
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. -->
# bert-finetuned-ner-adam
This model is a fine-tuned version of [dslim/bert-large-NER](https://huggingface.co/dslim/bert-large-NER) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.8340
- Recall: 0.8131
- F1: 0.8234
- Accuracy: 0.9216
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1744 | 1.0 | 893 | nan | 0.8276 | 0.8115 | 0.8195 | 0.9205 |
| 0.128 | 2.0 | 1786 | nan | 0.8404 | 0.8256 | 0.8329 | 0.9238 |
| 0.0768 | 3.0 | 2679 | nan | 0.8340 | 0.8131 | 0.8234 | 0.9216 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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