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---
license: mit
base_model: dslim/bert-large-NER
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Adam-NER-Model
results: []
datasets:
- conll2003
- rungalileo/mit_movies
- hyperhustle/ner-dataset
language:
- en
pipeline_tag: token-classification
---
<!-- 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 [hyperhustle/ner-dataset](https://huggingface.co/datasets/hyperhustle/ner-dataset) dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.8845
- Recall: 0.8749
- F1: 0.8797
- Accuracy: 0.9646
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0949 | 1.0 | 3080 | nan | 0.8914 | 0.8942 | 0.8928 | 0.9663 |
| 0.0574 | 2.0 | 6160 | nan | 0.8763 | 0.8784 | 0.8773 | 0.9635 |
| 0.0376 | 3.0 | 9240 | nan | 0.8845 | 0.8749 | 0.8797 | 0.9646 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |