---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-word-embedding-model
  results: []
datasets:
- lsoni/combined_tweetner7_word_embedding_augmented_dataset
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 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 lsoni/combined_tweetner7_word_embedding_augmented_dataset (combined training dataset tweetner7(train_2021)+augmented dataset(train_2021) using word embedding technique).
and it uses evaluation dataset the lsoni/combined_tweetner7_word_embedding_augmented_dataset_eval (combined training dataset tweetner7(validation_2021)+augmented eval dataset(validation_2021) using word embedding technique).
It achieves the following results on the evaluation set:
- Loss: 0.5447
- Precision: 0.6541
- Recall: 0.4910
- F1: 0.5610
- Accuracy: 0.8623

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7226        | 1.0   | 624  | 0.5818          | 0.7102    | 0.4474 | 0.5490 | 0.8628   |
| 0.5246        | 2.0   | 1248 | 0.5462          | 0.6465    | 0.4807 | 0.5514 | 0.8615   |
| 0.4558        | 3.0   | 1872 | 0.5447          | 0.6541    | 0.4910 | 0.5610 | 0.8623   |


### Framework versions

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