Instructions to use abidlabs/trackio-demo-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abidlabs/trackio-demo-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="abidlabs/trackio-demo-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("abidlabs/trackio-demo-model") model = AutoModelForSequenceClassification.from_pretrained("abidlabs/trackio-demo-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from abidlabs/trackio-demo-model: direct link, hf CLI and curl.
- Browser
- Download file 1.6 kB
-
https://huggingface.co/abidlabs/trackio-demo-model/resolve/main/README.md
- Command line
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hf download hf://abidlabs/trackio-demo-model/README.md
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curl -L -o README.md https://huggingface.co/abidlabs/trackio-demo-model/resolve/main/README.md
1.6 kB
metadata
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- trackio
- trackio:https://huggingface.co/spaces/abidlabs/trackio
- >-
trackio:https://huggingface.co/spaces/abidlabs/huggingface-z2Cawosa6itL4l1LakX80g
- generated_from_trainer
model-index:
- name: trackio-demo-model
results: []
trackio-demo-model
This model is a fine-tuned version of distilbert-base-uncased 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
Training results
Framework versions
- Transformers 5.0.0rc2
- Pytorch 2.9.1
- Datasets 4.4.2
- Tokenizers 0.22.2
