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--- |
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tags: |
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- autotrain |
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- token-classification |
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language: |
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- en |
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widget: |
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- text: >- |
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He at the last appointed him on one, And let all others from his hearte gon, |
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And chose her of his own authority; For love is blind all day, and may not |
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see. |
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- text: >- |
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I’m sorry but I just can’t seem to wrap my head around it. - I’m sorry but I |
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just can’t seem to understand. |
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- text: Why are you so bent out of shape? - Why are you so upset? |
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- text: Listen, it is easier said than done, many people lack commitment. |
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co2_eq_emissions: |
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emissions: 0.04215761331893144 |
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license: mit |
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library_name: transformers |
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pipeline_tag: token-classification |
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--- |
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# Fine-tune datasets |
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- MAGPIE corpus: https://aclanthology.org/2020.lrec-1.35/ |
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- EPIE corpus: https://link.springer.com/content/pdf/10.1007/978-3-030-58323-1.pdf |
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# Model Trained Using AutoTrain |
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- Problem type: Entity Extraction |
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- Model ID: 1595156286 |
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- CO2 Emissions (in grams): 0.0422 |
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## Validation Metrics |
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- Loss: 0.012 |
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- Accuracy: 0.996 |
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- Precision: 0.000 |
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- Recall: 0.000 |
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- F1: 0.000 |
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## Usage |
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### You can use cURL to access this model: |
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``` |
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$ 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 |
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``` |
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### Or Python API: |
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``` |
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from transformers import AutoModelForTokenClassification, AutoTokenizer |
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model = AutoModelForTokenClassification.from_pretrained("imranraad/autotrain-magpie-epie-combine-xlmr-metaphor-1595156286", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("imranraad/autotrain-magpie-epie-combine-xlmr-metaphor-1595156286", use_auth_token=True) |
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inputs = tokenizer("I love AutoTrain", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
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### How to get the idioms: |
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``` |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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model = AutoModelForTokenClassification.from_pretrained("imranraad/idiom-xlm-roberta") |
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tokenizer = AutoTokenizer.from_pretrained("imranraad/idiom-xlm-roberta") |
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pipeline_idioms = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") |
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text = "Why are you so bent out of shape? - Why are you so upset?" |
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idioms = pipeline_idioms(text) |
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for idiom in idioms: |
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if idiom['entity_group'] == '1': |
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print(idiom['word']) |
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``` |
