Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
text-embeddings-inference
Instructions to use henryscheible/eval_qnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/eval_qnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/eval_qnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/eval_qnli") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/eval_qnli") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
model-index:
- name: eval_qnli
results: []
eval_qnli
This model is a fine-tuned version of bert-base-uncased on the GLUE QNLI 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1