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---
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>
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