transformers pytorch xlm-roberta token-classification en arxiv:2209.12616 arxiv:1910.09700 autotrain_compatible endpoints_compatible region:us
---
language:
- en
---
# Model Card for XLM-RoBERTa for NER
 
XLM-RoBERTa finetuned on NER.
 
# Model Details
 
## Model Description
 
XLM-RoBERTa finetuned on NER.
- **Developed by:** Asahi Ushio
- **Shared by [Optional]:** Hugging Face
- **Model type:** Token Classification
- **Language(s) (NLP):** en
- **License:** More information needed
- **Related Models:** XLM-RoBERTa
  - **Parent Model:** XLM-RoBERTa
- **Resources for more information:** 
    - [GitHub Repo](https://github.com/asahi417/tner) 
    - [Associated Paper](https://arxiv.org/abs/2209.12616) 
    - [Space](https://huggingface.co/spaces/akdeniz27/turkish-named-entity-recognition)
 
# Uses
 
 
## Direct Use
Token Classification
 
 
## Downstream Use [Optional]
 
This model can be used in conjunction with the [tner library](https://github.com/asahi417/tner).
 
## 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 recomendations.
 
 
# Training Details
 
## Training Data
 
An NER dataset contains a sequence of tokens and tags for each split (usually `train`/`validation`/`test`),
```python
{
    'train': {
        'tokens': [
            ['@paulwalk', 'It', "'s", 'the', 'view', 'from', 'where', 'I', "'m", 'living', 'for', 'two', 'weeks', '.', 'Empire', 'State', 'Building', '=', 'ESB', '.', 'Pretty', 'bad', 'storm', 'here', 'last', 'evening', '.'],
            ['From', 'Green', 'Newsfeed', ':', 'AHFA', 'extends', 'deadline', 'for', 'Sage', 'Award', 'to', 'Nov', '.', '5', 'http://tinyurl.com/24agj38'], ...
        ],
        'tags': [
            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
            [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ...
        ]
    },
    'validation': ...,
    'test': ...,
}
```
with a dictionary to map a label to its index (`label2id`) as below.
```python
{"O": 0, "B-ORG": 1, "B-MISC": 2, "B-PER": 3, "I-PER": 4, "B-LOC": 5, "I-ORG": 6, "I-MISC": 7, "I-LOC": 8}
```
 
 
 
 
## Training Procedure
 
### Preprocessing
 
More information needed
 
### Speeds, Sizes, Times
 
**Layer_norm_eps:** 1e-05,
**Num_attention_heads:** 12,
**Num_hidden_layers:** 12,
**Vocab_size:** 250002
 
# Evaluation

 
## Testing Data, Factors & Metrics
 
### Testing Data
 
See [dataset card](https://github.com/asahi417/tner/blob/master/DATASET_CARD.md) for full dataset lists
 
### 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
- **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:**
 
```
@inproceedings{ushio-camacho-collados-2021-ner,
    title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
    author = "Ushio, Asahi  and
      Camacho-Collados, Jose",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.eacl-demos.7",
    pages = "53--62",
}
```
 
 
# Glossary [optional]
 
More information needed
 
# More Information [optional]
More information needed
 
# Model Card Authors [optional]
 
Asahi Ushio 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("asahi417/tner-xlm-roberta-base-ontonotes5")
  
  model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-base-ontonotes5")
 
```

</details>
