Instructions to use huawei-noah/EntityCS-39-PEP_MS_MLM-xlmr-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huawei-noah/EntityCS-39-PEP_MS_MLM-xlmr-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="huawei-noah/EntityCS-39-PEP_MS_MLM-xlmr-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("huawei-noah/EntityCS-39-PEP_MS_MLM-xlmr-base") model = AutoModelForMaskedLM.from_pretrained("huawei-noah/EntityCS-39-PEP_MS_MLM-xlmr-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Usage
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The current model can be used for further fine-tuning on downstream tasks.
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In the paper, we focused on entity-related tasks, such as NER, Word Sense Disambiguation
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## How to Get Started with the Model
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## Usage
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The current model can be used for further fine-tuning on downstream tasks.
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In the paper, we focused on entity-related tasks, such as NER, Word Sense Disambiguation and Slot Filling.
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Alternatively, it can be used directly (no fine-tuning) for probing tasks, i.e. predict missing words, such as [X-FACTR](https://aclanthology.org/2020.emnlp-main.479/).
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## How to Get Started with the Model
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