---
language: en
tags:
- token-classification
- NER
- Biomedical
- Chemicals
datasets:
- BC5CDR-chemicals
- BC4CHEMD
license: apache-2.0 
---
​
​
# Model Card for biobert Chemical NER
 
# Model Details
 
## Model Description
 
BioBERT model fine-tuned in NER task with BC5CDR-chemicals and BC4CHEMD corpus.
 
- **Developed by:** librAIry
- **Shared by [Optional]:** Alvaro A
- **Model type:** Token Classification 
- **Language(s) (NLP):** More information needed
- **License:** Apache 2.0
- **Parent Model:** NER
- **Resources for more information:**
  - [GitHub Repo](https://github.com/librairy/bio-ner)
   - [Associated Paper](https://oa.upm.es/67933/)
 	
​
​
# Uses 
​
## Direct Use
This model can be used for the task of model is lost/undocumented.
It was fine-tuned in order to use it in a BioNER/BioNEN system which is available at the [GitHub Repo](https://github.com/librairy/bio-ner)
 
## Downstream Use [Optional]
 
More information needed.
 
## Out-of-Scope Use
 
The model should not be used to intentionally create hostile or alienating environments for people. 
 
# Bias, Risks, and Limitations
 
 
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
​
​
​
## Recommendations
 
 
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
​
# Training Details
 
## Training Data
 
More information needed 
 
## Training Procedure
​
 
### Preprocessing
 
More information needed 
 
 
​
​
 
### Speeds, Sizes, Times
More information needed 
​
 
# Evaluation
 
 
## Testing Data, Factors & Metrics
 
### Testing Data
 
More information needed 
 
 
### Factors
More information needed
 
### Metrics
 
More information needed
 
 
## Results 
 
More information needed
​
 
# Model Examination
 
More information needed
 
# Environmental Impact
 
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
- **Hardware Type:** More information needed
	- **Fine-tuning process**:  was done in Google Collab using a TPU.
- **Hours used:** More information needed
- **Cloud Provider:** More information needed
- **Compute Region:** More information needed
- **Carbon Emitted:** More information needed
 
# Technical Specifications [optional]
 
## Model Architecture and Objective
​
More information needed 
 
## Compute Infrastructure
 
More information needed 
 
### Hardware
 
 
More information needed
 
### Software
 
More information needed.
 
# Citation
​
 
**BibTeX:**
 
 
More information needed.
 
 
 
 
# Glossary [optional]
More information needed 
 
# More Information [optional]
More information needed 
​
 
# Model Card Authors [optional]
 
Alvaro A in collaboration with Ezi Ozoani and the Hugging Face team
​
​
# Model Card Contact
 
More information needed
 
# How to Get Started with the Model
 
Use the code below to get started with the model.
 
<details>
<summary> Click to expand </summary>
​
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("alvaroalon2/biobert_chemical_ner")
model = AutoModelForTokenClassification.from_pretrained("alvaroalon2/biobert_chemical_ner") 
 ```
</details>