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---
language:
- en
tags:
- pytorch
- ner
- qa
inference: false
license: mit
datasets:
- conll2003
metrics:
- f1
---
# t5-base-qa-ner-conll
Unofficial implementation of [InstructionNER](https://arxiv.org/pdf/2203.03903v1.pdf).
t5-base model tuned on conll2003 dataset.
https://github.com/ovbystrova/InstructionNER
## Inference
```shell
git clone https://github.com/ovbystrova/InstructionNER
cd InstructionNER
```
```python
from instruction_ner.model import Model
model = Model(
model_path_or_name="olgaduchovny/t5-base-qa-ner-conll",
tokenizer_path_or_name="olgaduchovny/t5-base-qa-ner-conll"
)
options = ["LOC", "PER", "ORG", "MISC"]
instruction = "please extract entities and their types from the input sentence, " \
"all entity types are in options"
text = "The protest , which attracted several thousand supporters , coincided with the 18th anniversary of Spain 's constitution ."
generation_kwargs = {
"num_beams": 2,
"max_length": 128
}
pred_spans = model.predict(
text=text,
generation_kwargs=generation_kwargs,
instruction=instruction,
options=options
)
>>> [(99, 104, 'LOC')]
```
## Prediction Sample
```
Sentence: The protest , which attracted several thousand supporters , coincided with the 18th anniversary of Spain 's constitution .
Instruction: please extract entities and their types from the input sentence, all entity types are in options
Options: ORG, PER, LOC
Prediction (raw text): Spain is a LOC.
Prediction (span): [(99, 104, 'LOC')]
```
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