Instructions to use tydymy/human_dna_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tydymy/human_dna_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tydymy/human_dna_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tydymy/human_dna_classification") model = AutoModelForSequenceClassification.from_pretrained("tydymy/human_dna_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from tydymy/human_dna_classification: direct link, hf CLI and curl.
- Browser
- Download file 1.65 kB
-
https://huggingface.co/tydymy/human_dna_classification/resolve/main/README.md
- Command line
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hf download hf://tydymy/human_dna_classification/README.md
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curl -L -o README.md https://huggingface.co/tydymy/human_dna_classification/resolve/main/README.md
1.65 kB
metadata
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-500m-human-ref
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: human_classification
results: []
human_classification
This model is a fine-tuned version of InstaDeepAI/nucleotide-transformer-500m-human-ref on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1830
- Accuracy: 0.9589
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: 256
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1024
- 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 | Accuracy |
|---|---|---|---|---|
| 0.0924 | 1.0 | 615 | 0.1074 | 0.9572 |
| 0.0587 | 2.0 | 1230 | 0.1217 | 0.9569 |
| 0.0137 | 3.0 | 1845 | 0.1830 | 0.9589 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1