Instructions to use netgvarun2005/qa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use netgvarun2005/qa_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="netgvarun2005/qa_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("netgvarun2005/qa_model") model = AutoModelForQuestionAnswering.from_pretrained("netgvarun2005/qa_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from netgvarun2005/qa_model: direct link, hf CLI and curl.
- Browser
- Download file 1.74 kB
-
https://huggingface.co/netgvarun2005/qa_model/resolve/main/README.md
- Command line
-
hf download hf://netgvarun2005/qa_model/README.md
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curl -L -o README.md https://huggingface.co/netgvarun2005/qa_model/resolve/main/README.md
1.74 kB
metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: qa_model
results: []
qa_model
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: 2.2864
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 403 | 1.6619 |
| 2.4962 | 2.0 | 806 | 1.5064 |
| 1.3281 | 3.0 | 1209 | 1.5355 |
| 0.943 | 4.0 | 1612 | 1.6105 |
| 0.6258 | 5.0 | 2015 | 1.7803 |
| 0.6258 | 6.0 | 2418 | 1.8905 |
| 0.3992 | 7.0 | 2821 | 2.0605 |
| 0.2949 | 8.0 | 3224 | 2.2018 |
| 0.2103 | 9.0 | 3627 | 2.2359 |
| 0.1716 | 10.0 | 4030 | 2.2864 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0