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--- |
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language: |
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- en |
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tags: |
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- ner |
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- gene |
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- protein |
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- rna |
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- bioinfomatics |
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license: apache-2.0 |
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datasets: |
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- jnlpba |
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widget: |
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- text: "It consists of 25 exons encoding a 1,278-amino acid glycoprotein that is composed of 13 transmembrane domains" |
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--- |
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# NER to find Gene & Gene products |
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> The model was trained on jnlpba dataset, pretrained on this [pubmed-pretrained roberta model](/raynardj/roberta-pubmed) |
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All the labels, the possible token classes. |
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```json |
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{"label2id": { |
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"DNA": 2, |
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"O": 0, |
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"RNA": 5, |
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"cell_line": 4, |
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"cell_type": 3, |
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"protein": 1 |
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} |
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} |
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``` |
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Notice, we removed the 'B-','I-' etc from data label.🗡 |
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## This is the template we suggest for using the model |
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```python |
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from transformers import pipeline |
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PRETRAINED = "raynardj/ner-gene-dna-rna-jnlpba-pubmed" |
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ner = pipeline(task="ner",model=PRETRAINED, tokenizer=PRETRAINED) |
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ner("Your text", aggregation_strategy="first") |
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``` |
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And here is to make your output more consecutive ⭐️ |
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```python |
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import pandas as pd |
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from transformers import AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained(PRETRAINED) |
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def clean_output(outputs): |
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results = [] |
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current = [] |
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last_idx = 0 |
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# make to sub group by position |
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for output in outputs: |
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if output["index"]-1==last_idx: |
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current.append(output) |
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else: |
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results.append(current) |
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current = [output, ] |
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last_idx = output["index"] |
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if len(current)>0: |
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results.append(current) |
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# from tokens to string |
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strings = [] |
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for c in results: |
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tokens = [] |
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starts = [] |
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ends = [] |
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for o in c: |
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tokens.append(o['word']) |
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starts.append(o['start']) |
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ends.append(o['end']) |
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new_str = tokenizer.convert_tokens_to_string(tokens) |
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if new_str!='': |
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strings.append(dict( |
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word=new_str, |
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start = min(starts), |
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end = max(ends), |
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entity = c[0]['entity'] |
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)) |
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return strings |
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def entity_table(pipeline, **pipeline_kw): |
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if "aggregation_strategy" not in pipeline_kw: |
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pipeline_kw["aggregation_strategy"] = "first" |
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def create_table(text): |
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return pd.DataFrame( |
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clean_output( |
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pipeline(text, **pipeline_kw) |
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) |
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) |
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return create_table |
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# will return a dataframe |
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entity_table(ner)(YOUR_VERY_CONTENTFUL_TEXT) |
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``` |
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> check our NER model on |
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* [gene and gene products](/raynardj/ner-gene-dna-rna-jnlpba-pubmed) |
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* [chemical substance](/raynardj/ner-chemical-bionlp-bc5cdr-pubmed). |
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* [disease](/raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed) |
