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
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README.md
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This model is specialized for handling SEC filings. We expanded the training set by adding 3.1 billion tokens from the SEC filings corpus dataset. The dataset is sourced from EDGAR-CORPUS: Billions of Tokens Make The World Go Round (Loukas et al., ECONLP 2021) and can be downloaded from Zenodo. ๐
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**Types of Training Corpora ๐**
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This model is specialized for handling SEC filings. We expanded the training set by adding 3.1 billion tokens from the SEC filings corpus dataset. The dataset is sourced from EDGAR-CORPUS: Billions of Tokens Make The World Go Round (Loukas et al., ECONLP 2021) and can be downloaded from Zenodo. ๐
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The method to load a tokenizer and a model.
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For the FiLM model, you can call 'roberta-base' from the tokenizer.
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```python
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tokenizer = AutoTokenizer.from_pretrained('roberta-base')
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model = AutoModel.from_pretrained('HYdsl/FiLM')
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```
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**Types of Training Corpora ๐**
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