Instructions to use mateiaass/albertbase-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mateiaass/albertbase-qa 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="mateiaass/albertbase-qa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mateiaass/albertbase-qa") model = AutoModelForQuestionAnswering.from_pretrained("mateiaass/albertbase-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from mateiaass/albertbase-qa: direct link, hf CLI and curl.
- Browser
- Download file 1.39 kB
-
https://huggingface.co/mateiaass/albertbase-qa/resolve/main/README.md
- Command line
-
hf download hf://mateiaass/albertbase-qa/README.md
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curl -L -o README.md https://huggingface.co/mateiaass/albertbase-qa/resolve/main/README.md
1.39 kB
metadata
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: albertbase-qa
results: []
albertbase-qa
This model is a fine-tuned version of albert-base-v2 on the squad dataset. It achieves the following results on the evaluation set:
- Loss: 1.0115
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 |
|---|---|---|---|
| 0.897 | 1.0 | 4380 | 0.9106 |
| 0.671 | 2.0 | 8760 | 0.8892 |
| 0.476 | 3.0 | 13140 | 1.0115 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1