Instructions to use aayush9/saved_model_AAPL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aayush9/saved_model_AAPL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aayush9/saved_model_AAPL")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aayush9/saved_model_AAPL") model = AutoModelForSequenceClassification.from_pretrained("aayush9/saved_model_AAPL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from aayush9/saved_model_AAPL: direct link, hf CLI and curl.
- Browser
- Download file 582 Bytes
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https://huggingface.co/aayush9/saved_model_AAPL/resolve/main/README.md
- Command line
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hf download hf://aayush9/saved_model_AAPL/README.md
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curl -L -o README.md https://huggingface.co/aayush9/saved_model_AAPL/resolve/main/README.md
582 Bytes
| language: | |
| - en | |
| tags: | |
| - finance | |
| - stock-prediction | |
| - BERT | |
| - transformers | |
| license: mit | |
| datasets: | |
| - your-dataset-name | |
| metrics: | |
| - Mean Absolute Error | |
| base_model: bert-base-uncased | |
| task: regression | |
| evaluation_results: | |
| - task: | |
| type: stock-price-prediction | |
| metrics: | |
| - name: Mean Absolute Error (MAE) | |
| value: | |
| - Insert MAE value here | |
| source: | |
| name: Your Evaluation Dataset Name | |
| url: Link to your evaluation dataset | |
| citation: | |
| author: Your Name | |
| year: 2024 | |
| conference: Your Conference Name | |
| arxiv_id: your-arxiv-id-if-applicable | |
| library_name: transformers | |