Instructions to use laboyle1/distilbert-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use laboyle1/distilbert-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="laboyle1/distilbert-finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("laboyle1/distilbert-finetuned") model = AutoModelForMaskedLM.from_pretrained("laboyle1/distilbert-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-finetuned
results: []
distilbert-finetuned
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.9895
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.2103 | 1.0 | 10024 | 2.0834 |
| 2.1146 | 2.0 | 20048 | 2.0387 |
| 2.0721 | 3.0 | 30072 | 2.0095 |
Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1