---
base_model: google-bert/bert-large-uncased
datasets:
- wnut_17
library_name: peft
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: bert-large-uncased-wnut_17
  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-large-uncased-wnut_17

This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3198
- Precision: 0.3458
- Recall: 0.2308
- F1: 0.2768
- Accuracy: 0.9344

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 213  | 0.4550          | 1.0       | 0.0    | 0.0    | 0.9256   |
| No log        | 2.0   | 426  | 0.4535          | 1.0       | 0.0    | 0.0    | 0.9256   |
| 0.5372        | 3.0   | 639  | 0.4368          | 1.0       | 0.0    | 0.0    | 0.9256   |
| 0.5372        | 4.0   | 852  | 0.3536          | 0.1268    | 0.0083 | 0.0157 | 0.9258   |
| 0.2367        | 5.0   | 1065 | 0.3517          | 0.2264    | 0.0621 | 0.0975 | 0.9267   |
| 0.2367        | 6.0   | 1278 | 0.3463          | 0.3471    | 0.1094 | 0.1663 | 0.9300   |
| 0.2367        | 7.0   | 1491 | 0.3320          | 0.3424    | 0.1640 | 0.2218 | 0.9319   |
| 0.1954        | 8.0   | 1704 | 0.3295          | 0.3436    | 0.1854 | 0.2408 | 0.9333   |
| 0.1954        | 9.0   | 1917 | 0.3201          | 0.3441    | 0.2261 | 0.2729 | 0.9343   |
| 0.1816        | 10.0  | 2130 | 0.3198          | 0.3458    | 0.2308 | 0.2768 | 0.9344   |


### Framework versions

- PEFT 0.12.1.dev0
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1