Instructions to use dreaming-tree/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dreaming-tree/tmp_trainer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, RobertaTaggerForMultiSpanQA tokenizer = AutoTokenizer.from_pretrained("dreaming-tree/tmp_trainer") model = RobertaTaggerForMultiSpanQA.from_pretrained("dreaming-tree/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from dreaming-tree/tmp_trainer: direct link, hf CLI and curl.
- Browser
- Download file 906 Bytes
-
https://huggingface.co/dreaming-tree/tmp_trainer/resolve/main/README.md
- Command line
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hf download hf://dreaming-tree/tmp_trainer/README.md
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curl -L -o README.md https://huggingface.co/dreaming-tree/tmp_trainer/resolve/main/README.md
906 Bytes
metadata
tags:
- generated_from_trainer
model-index:
- name: tmp_trainer
results: []
tmp_trainer
This model was trained from scratch on an unknown dataset.
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: 5e-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
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.10.0
- Tokenizers 0.13.2