Instructions to use ratno/ub_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ratno/ub_model with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="ratno/ub_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ratno/ub_model") model = AutoModelForQuestionAnswering.from_pretrained("ratno/ub_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ratno/ub_model: direct link, hf CLI and curl.
- Browser
- Download file 1.36 kB
-
https://huggingface.co/ratno/ub_model/resolve/refs%2Fpr%2F2/README.md
- Command line
-
hf download hf://ratno/ub_model@refs/pr/2/README.md
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curl -L -o README.md https://huggingface.co/ratno/ub_model/resolve/refs%2Fpr%2F2/README.md
1.36 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: ub_model
results: []
ub_model
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.1722
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.464 | 1.0 | 6516 | 2.3544 |
| 2.2053 | 2.0 | 13032 | 2.2005 |
| 1.9738 | 3.0 | 19548 | 2.1722 |
Framework versions
- Transformers 4.30.1
- Pytorch 1.13.1+cu116
- Datasets 2.12.0
- Tokenizers 0.13.3