Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use jkhan447/sentiment-model-sample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jkhan447/sentiment-model-sample with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jkhan447/sentiment-model-sample")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jkhan447/sentiment-model-sample") model = AutoModelForSequenceClassification.from_pretrained("jkhan447/sentiment-model-sample", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from jkhan447/sentiment-model-sample: direct link, hf CLI and curl.
- Browser
- Download file 1.39 kB
-
https://huggingface.co/jkhan447/sentiment-model-sample/resolve/main/README.md
- Command line
-
hf download hf://jkhan447/sentiment-model-sample/README.md
-
curl -L -o README.md https://huggingface.co/jkhan447/sentiment-model-sample/resolve/main/README.md
1.39 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: sentiment-model-sample
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93948
sentiment-model-sample
This model is a fine-tuned version of bert-base-uncased on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 0.5280
- Accuracy: 0.9395
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6