Instructions to use MRF18/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MRF18/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MRF18/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MRF18/results") model = AutoModelForSequenceClassification.from_pretrained("MRF18/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from MRF18/results: direct link, hf CLI and curl.
- Browser
- Download file 1.1 kB
-
https://huggingface.co/MRF18/results/resolve/main/README.md
- Command line
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hf download hf://MRF18/results/README.md
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curl -L -o README.md https://huggingface.co/MRF18/results/resolve/main/README.md
1.1 kB
metadata
license: mit
tags:
- generated_from_trainer
model-index:
- name: results
results: []
results
This model is a fine-tuned version of MRF18/results on the None 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1