---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-word-embedding-model
  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-word-embedding-model

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the the combined training dataset(tweetner7(train_2021)+augmented dataset(train_2021) 
using word embedding technique).

Training Dataset: lsoni/combined_tweetner7_word_embedding_augmented_dataset

Evaluation Dataset: lsoni/combined_tweetner7_word_embedding_augmented_dataset_eval

It achieves the following results on the evaluation set:
- Loss: 0.5411
- Precision: 0.6710
- Recall: 0.5062
- F1: 0.5771
- Accuracy: 0.8650

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7157        | 1.0   | 624  | 0.5842          | 0.6958    | 0.4498 | 0.5464 | 0.8608   |
| 0.5299        | 2.0   | 1248 | 0.5449          | 0.6662    | 0.4897 | 0.5645 | 0.8635   |
| 0.4648        | 3.0   | 1872 | 0.5411          | 0.6710    | 0.5062 | 0.5771 | 0.8650   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.1
- Datasets 2.10.1
- Tokenizers 0.12.1