Instructions to use vaalto/test_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vaalto/test_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vaalto/test_trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vaalto/test_trainer") model = AutoModelForSequenceClassification.from_pretrained("vaalto/test_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from vaalto/test_trainer: direct link, hf CLI and curl.
- Browser
- Download file 1.67 kB
-
https://huggingface.co/vaalto/test_trainer/resolve/main/README.md
- Command line
-
hf download hf://vaalto/test_trainer/README.md
-
curl -L -o README.md https://huggingface.co/vaalto/test_trainer/resolve/main/README.md
1.67 kB
metadata
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: test_trainer
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.82
test_trainer
This model is a fine-tuned version of bert-base-cased on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 0.5366
- Accuracy: 0.82
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: 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 25 | 0.6720 | 0.58 |
| No log | 2.0 | 50 | 0.5366 | 0.82 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cpu
- Datasets 2.14.5
- Tokenizers 0.13.3