metadata

tags:
  - autotrain
  - token-classification
language:
  - en
widget:
  - text: >-
      He at the last appointed him on one, And let all others from his hearte
      gon, And chose her of his own authority; For love is blind all day, and
      may not see.
  - text: >-
      I’m sorry but I just can’t seem to wrap my head around it. - I’m sorry but
      I just can’t seem to understand.
  - text: Why are you so bent out of shape? - Why are you so upset?
  - text: Listen, it is easier said than done, many people lack commitment.
co2_eq_emissions:
  emissions: 0.04215761331893144
license: mit
library_name: transformers
pipeline_tag: token-classification

Fine-tune datasets

MAGPIE corpus: https://aclanthology.org/2020.lrec-1.35/
EPIE corpus: https://link.springer.com/content/pdf/10.1007/978-3-030-58323-1.pdf

Model Trained Using AutoTrain

Problem type: Entity Extraction
Model ID: 1595156286
CO2 Emissions (in grams): 0.0422

Validation Metrics

Loss: 0.012
Accuracy: 0.996
Precision: 0.000
Recall: 0.000
F1: 0.000

Usage

You can use cURL to access this model:

$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/imranraad/autotrain-magpie-epie-combine-xlmr-metaphor-1595156286

Or Python API:

from transformers import AutoModelForTokenClassification, AutoTokenizer

model = AutoModelForTokenClassification.from_pretrained("imranraad/autotrain-magpie-epie-combine-xlmr-metaphor-1595156286", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("imranraad/autotrain-magpie-epie-combine-xlmr-metaphor-1595156286", use_auth_token=True)

inputs = tokenizer("I love AutoTrain", return_tensors="pt")

outputs = model(**inputs)

How to get the idioms:

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

model = AutoModelForTokenClassification.from_pretrained("imranraad/idiom-xlm-roberta")

tokenizer = AutoTokenizer.from_pretrained("imranraad/idiom-xlm-roberta")

pipeline_idioms = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Why are you so bent out of shape? - Why are you so upset?"

idioms = pipeline_idioms(text)
for idiom in idioms:
    if idiom['entity_group'] == '1':
        print(idiom['word'])