Edit model card

Fine-tune datasets

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'])
Downloads last month
60
Hosted inference API
Token Classification
Examples
Examples
This model can be loaded on the Inference API on-demand.