Instructions to use kbalde/distilbert-base-uncased-finetuned-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kbalde/distilbert-base-uncased-finetuned-imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kbalde/distilbert-base-uncased-finetuned-imdb")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kbalde/distilbert-base-uncased-finetuned-imdb") model = AutoModelForMaskedLM.from_pretrained("kbalde/distilbert-base-uncased-finetuned-imdb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 2.4742
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.7069 | 1.0 | 157 | 2.4948 |
| 2.5792 | 2.0 | 314 | 2.4235 |
| 2.5259 | 3.0 | 471 | 2.4348 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2