Instructions to use phdev/dynamic_tinybert-finetuned-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phdev/dynamic_tinybert-finetuned-squad 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="phdev/dynamic_tinybert-finetuned-squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("phdev/dynamic_tinybert-finetuned-squad") model = AutoModelForQuestionAnswering.from_pretrained("phdev/dynamic_tinybert-finetuned-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from phdev/dynamic_tinybert-finetuned-squad: direct link, hf CLI and curl.
- Browser
- Download file 1.44 kB
-
https://huggingface.co/phdev/dynamic_tinybert-finetuned-squad/resolve/main/README.md
- Command line
-
hf download hf://phdev/dynamic_tinybert-finetuned-squad/README.md
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curl -L -o README.md https://huggingface.co/phdev/dynamic_tinybert-finetuned-squad/resolve/main/README.md
1.44 kB
metadata
license: apache-2.0
base_model: Intel/dynamic_tinybert
tags:
- generated_from_trainer
model-index:
- name: dynamic_tinybert-finetuned-squad
results: []
dynamic_tinybert-finetuned-squad
This model is a fine-tuned version of Intel/dynamic_tinybert on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4682
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 |
|---|---|---|---|
| 0.5086 | 1.0 | 5533 | 1.1495 |
| 0.3869 | 2.0 | 11066 | 1.2774 |
| 0.2659 | 3.0 | 16599 | 1.4682 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0