
# NER to find Gene & Gene products
> The model was trained on bionlp and bc4cdr dataset, pretrained on this [pubmed-pretrained roberta model](/raynardj/roberta-pubmed)
All the labels, the possible token classes.
```json
{"label2id":
  {
    "O": 0,
    "Chemical": 1,
  }
 }
```
 
Notice, we removed the 'B-','I-' etc from data label.🗡
 
## This is the template we suggest for using the model
Of course I'm well aware of the ```aggregation_strategy``` arguments offered by hf, but by the way of training, I discard any entropy loss for appending subwords, like only the label for the 1st subword token is not -100, after many search effort, I can't find a way to achieve that with default pipeline, hence I fancy an inference class myself.
```python
!pip install forgebox
from forgebox.hf.train import NERInference
ner = NERInference.from_pretrained("raynardj/ner-chemical-bionlp-bc5cdr-pubmed")
a_df = ner.predict(["text1", "text2"])
```

> check our NER model on
* [gene and gene products](/raynardj/ner-gene-dna-rna-jnlpba-pubmed)
* [chemical substance](/raynardj/ner-chemical-bionlp-bc5cdr-pubmed).
* [disease](/raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed)
{"language": ["en"], "license": "apache-2.0", "tags": ["ner", "chemical", "bionlp", "bc4cdr", "bioinfomatics"], "datasets": ["bionlp", "bc4cdr"], "widget": [{"text": "Serotonin receptor 2A (HTR2A) gene polymorphism predicts treatment response to venlafaxine XR in generalized anxiety disorder."}]}
transformers pytorch roberta token-classification ner chemical bionlp bc4cdr bioinfomatics en dataset:bionlp dataset:bc4cdr license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #token-classification #ner #chemical #bionlp #bc4cdr #bioinfomatics #en #dataset-bionlp #dataset-bc4cdr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# NER to find Gene & Gene products
> The model was trained on bionlp and bc4cdr dataset, pretrained on this pubmed-pretrained roberta model
All the labels, the possible token classes.

 
Notice, we removed the 'B-','I-' etc from data label.
 
## This is the template we suggest for using the model
Of course I'm well aware of the  arguments offered by hf, but by the way of training, I discard any entropy loss for appending subwords, like only the label for the 1st subword token is not -100, after many search effort, I can't find a way to achieve that with default pipeline, hence I fancy an inference class myself.


> check our NER model on
* gene and gene products
* chemical substance.
* disease
# NER to find Gene & Gene products
> The model was trained on bionlp and bc4cdr dataset, pretrained on this pubmed-pretrained roberta model
All the labels, the possible token classes.

 
Notice, we removed the 'B-','I-' etc from data label. ## This is the template we suggest for using the model
Of course I'm well aware of the  arguments offered by hf, but by the way of training, I discard any entropy loss for appending subwords, like only the label for the 1st subword token is not -100, after many search effort, I can't find a way to achieve that with default pipeline, hence I fancy an inference class myself.


> check our NER model on
* gene and gene products
* chemical substance.
* disease
TAGS
#transformers #pytorch #roberta #token-classification #ner #chemical #bionlp #bc4cdr #bioinfomatics #en #dataset-bionlp #dataset-bc4cdr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # NER to find Gene & Gene products
> The model was trained on bionlp and bc4cdr dataset, pretrained on this pubmed-pretrained roberta model
All the labels, the possible token classes.

 
Notice, we removed the 'B-','I-' etc from data label. ## This is the template we suggest for using the model
Of course I'm well aware of the  arguments offered by hf, but by the way of training, I discard any entropy loss for appending subwords, like only the label for the 1st subword token is not -100, after many search effort, I can't find a way to achieve that with default pipeline, hence I fancy an inference class myself.


> check our NER model on
* gene and gene products
* chemical substance.
* disease
passage: TAGS
#transformers #pytorch #roberta #token-classification #ner #chemical #bionlp #bc4cdr #bioinfomatics #en #dataset-bionlp #dataset-bc4cdr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# NER to find Gene & Gene products
> The model was trained on bionlp and bc4cdr dataset, pretrained on this pubmed-pretrained roberta model
All the labels, the possible token classes.

 
Notice, we removed the 'B-','I-' etc from data label.## This is the template we suggest for using the model
Of course I'm well aware of the  arguments offered by hf, but by the way of training, I discard any entropy loss for appending subwords, like only the label for the 1st subword token is not -100, after many search effort, I can't find a way to achieve that with default pipeline, hence I fancy an inference class myself.


> check our NER model on
* gene and gene products
* chemical substance.
* disease