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
metadata
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