Instructions to use HYdsl/FiLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HYdsl/FiLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HYdsl/FiLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("HYdsl/FiLM") model = AutoModelForMaskedLM.from_pretrained("HYdsl/FiLM") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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To train FiLM, we have categorized our Financial Corpus into specific groups and gathered a diverse range of corpora to ensure optimal performance.
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We offer two versions of the FiLM model, each tailored for specific use-cases in the Financial domain:
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**FiLM (2.4B): Our Base Model**
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To train FiLM, we have categorized our Financial Corpus into specific groups and gathered a diverse range of corpora to ensure optimal performance.
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Our model can be called Fin-RoBERTa.
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We offer two versions of the FiLM model, each tailored for specific use-cases in the Financial domain:
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**FiLM (2.4B): Our Base Model**
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