Text Classification
Transformers
PyTorch
TensorBoard
English
French
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Ahmedgr/DistilBert_Fine_tune_QuestionVsAnswer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ahmedgr/DistilBert_Fine_tune_QuestionVsAnswer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ahmedgr/DistilBert_Fine_tune_QuestionVsAnswer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ahmedgr/DistilBert_Fine_tune_QuestionVsAnswer") model = AutoModelForSequenceClassification.from_pretrained("Ahmedgr/DistilBert_Fine_tune_QuestionVsAnswer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Ahmedgr/DistilBert_Fine_tune_QuestionVsAnswer: direct link, hf CLI and curl.
- Browser
- Download file 975 Bytes
-
https://huggingface.co/Ahmedgr/DistilBert_Fine_tune_QuestionVsAnswer/resolve/main/README.md
- Command line
-
hf download hf://Ahmedgr/DistilBert_Fine_tune_QuestionVsAnswer/README.md
-
curl -L -o README.md https://huggingface.co/Ahmedgr/DistilBert_Fine_tune_QuestionVsAnswer/resolve/main/README.md
975 Bytes
metadata
tags:
- generated_from_trainer
model-index:
- name: DistilBert_Fine_tune_QuestionVsAnswer
results: []
license: mit
language:
- en
- fr
DistilBert_Fine_tune_QuestionVsAnswer
This model was trained from scratch on an unknown 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: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Tokenizers 0.11.6