|
--- |
|
library_name: peft |
|
tags: |
|
- parquet |
|
- text-classification |
|
datasets: |
|
- ag_news |
|
metrics: |
|
- accuracy |
|
base_model: nreimers/mmarco-mMiniLMv2-L6-H384-v1 |
|
model-index: |
|
- name: nreimers_mmarco-mMiniLMv2-L6-H384-v1-finetuned-lora-ag_news |
|
results: |
|
- task: |
|
type: text-classification |
|
name: Text Classification |
|
dataset: |
|
name: ag_news |
|
type: ag_news |
|
config: default |
|
split: test |
|
args: default |
|
metrics: |
|
- type: accuracy |
|
value: 0.8855263157894737 |
|
name: accuracy |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# nreimers_mmarco-mMiniLMv2-L6-H384-v1-finetuned-lora-ag_news |
|
|
|
This model is a fine-tuned version of [nreimers/mmarco-mMiniLMv2-L6-H384-v1](https://huggingface.co/nreimers/mmarco-mMiniLMv2-L6-H384-v1) on the ag_news dataset. |
|
It achieves the following results on the evaluation set: |
|
- accuracy: 0.8855 |
|
|
|
## 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: 0.0004 |
|
- train_batch_size: 24 |
|
- eval_batch_size: 24 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 4 |
|
|
|
### Training results |
|
|
|
| accuracy | train_loss | epoch | |
|
|:--------:|:----------:|:-----:| |
|
| 0.1899 | None | 0 | |
|
| 0.8628 | 0.5739 | 0 | |
|
| 0.8761 | 0.3828 | 1 | |
|
| 0.8841 | 0.3557 | 2 | |
|
| 0.8855 | 0.3432 | 3 | |
|
|
|
|
|
### Framework versions |
|
|
|
- PEFT 0.8.2 |
|
- Transformers 4.37.2 |
|
- Pytorch 2.2.0 |
|
- Datasets 2.16.1 |
|
- Tokenizers 0.15.2 |
