Instructions to use harsith05/imdb_practice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harsith05/imdb_practice with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="harsith05/imdb_practice")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("harsith05/imdb_practice") model = AutoModelForSequenceClassification.from_pretrained("harsith05/imdb_practice", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from harsith05/imdb_practice: direct link, hf CLI and curl.
- Browser
- Download file 1.63 kB
-
https://huggingface.co/harsith05/imdb_practice/resolve/main/README.md
- Command line
-
hf download hf://harsith05/imdb_practice/README.md
-
curl -L -o README.md https://huggingface.co/harsith05/imdb_practice/resolve/main/README.md
1.63 kB
metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: imdb_practice
results: []
imdb_practice
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3895
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.6567 | 1.0 | 782 | 0.4084 |
| 0.4219 | 2.0 | 1564 | 0.3984 |
| 0.4036 | 3.0 | 2346 | 0.3844 |
| 0.3582 | 4.0 | 3128 | 0.3861 |
| 0.3398 | 5.0 | 3910 | 0.3895 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2