library_name: peft
base_model: mistralai/Mistral-7B-v0.1
pipeline_tag: text-generation
Description: Named entity recognition
Original dataset: https://huggingface.co/datasets/conllpp
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Try querying this adapter for free in Lora Land at https://predibase.com/lora-land!
The adapter_category is Named Entity Recognition and the name is Named Entity Recognition (CoNLL++)
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Sample input: Your task is a Named Entity Recognition (NER) task. Predict the category of each entity, then place the entity into the list associated with the category in an output JSON payload. Below is an example:\nInput: EU rejects German call to boycott British lamb . Output: {"person": [], "organization": ["EU"], "location": [], "miscellaneous": ["German", "British"]}\nNow, complete the task.\nInput: By the close Yorkshire had turned that into a 37-run advantage but off-spinner Such had scuttled their hopes , taking four for 24 in 48 balls and leaving them hanging on 119 for five and praying for rain . Output:
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Sample output: {"person": ["Such"], "organization": ["Yorkshire"], "location": [], "miscellaneous": []}
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Try using this adapter yourself!
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mistral-7B-v0.1"
peft_model_id = "predibase/conllpp"
model = AutoModelForCausalLM.from_pretrained(model_id)
model.load_adapter(peft_model_id)