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---
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 |