Instructions to use zcode/distilbert-imdb-mlflow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zcode/distilbert-imdb-mlflow with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zcode/distilbert-imdb-mlflow")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zcode/distilbert-imdb-mlflow") model = AutoModelForSequenceClassification.from_pretrained("zcode/distilbert-imdb-mlflow") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-imdb-mlflow
results: []
distilbert-imdb-mlflow
This model is a fine-tuned version of distilbert-base-cased on the imdb dataset.
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0.5
Training results
Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0+cu117
- Datasets 2.13.1
- Tokenizers 0.14.1