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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## Exploring the Data Efficiency of Cross-Lingual Post-Training in Pretrained Language Models
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Paper: https://arxiv.org/abs/2310.13312
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Github: https://github.com/deep-over/FiLM
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**FiLM**(**Fi**nancial **L**anguage **M**odel) Models 🌟
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FiLM is a Pre-trained Language Model (PLM) optimized for the Financial domain, built upon a diverse range of Financial domain corpora. Initialized with the RoBERTa-base model, FiLM undergoes further training to achieve performance that surpasses RoBERTa-base in the economics sector for the first time.
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## Exploring the Data Efficiency of Cross-Lingual Post-Training in Pretrained Language Models
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(EMNLP 2023 findings)
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Paper: https://arxiv.org/abs/2310.13312
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Github: https://github.com/deep-over/FiLM
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### **FiLM**(**Fi**nancial **L**anguage **M**odel) Models 🌟
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FiLM is a Pre-trained Language Model (PLM) optimized for the Financial domain, built upon a diverse range of Financial domain corpora. Initialized with the RoBERTa-base model, FiLM undergoes further training to achieve performance that surpasses RoBERTa-base in the economics sector for the first time.
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