Edit model card

Description: 1500 PubMed articles with 4409 annotated chemicals, 5818 diseases and 3116 chemical-disease interactions.
Original dataset: https://huggingface.co/datasets/tner/bc5cdr
---
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 Chemical and Disease Recognition (bc5cdr)
---
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:

Input: "Naloxone reverses the antihypertensive effect of clonidine ."

Output: {'B-Chemical': ['Naloxone', 'clonidine'], 'B-Disease': [], 'I-Disease': [], 'I-Chemical': []}

Now, complete the task.

Input: "A standardized loading dose of VPA was administered , and venous blood was sampled at 0 , 1 , 2 , 3 , and 4 hours ."

Output:
---
Sample output: {'B-Chemical': ['VPA'], 'B-Disease': [], 'I-Disease': [], 'I-Chemical': []}
---
Try using this adapter yourself!

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "mistralai/Mistral-7B-v0.1"
peft_model_id = "predibase/bc5cdr"

model = AutoModelForCausalLM.from_pretrained(model_id)
model.load_adapter(peft_model_id)
Downloads last month
146
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for predibase/bc5cdr

Adapter
(1164)
this model

Collection including predibase/bc5cdr