# 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>

{"language": ["en"]}
transformers pytorch xlm-roberta token-classification en arxiv:2209.12616 arxiv:1910.09700 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #xlm-roberta #token-classification #en #arxiv-2209.12616 #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us 

# 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 
    - Associated Paper 
    - Space
 
# Uses
 
 
## Direct Use
Token Classification
 
 
## Downstream Use [Optional]
 
This model can be used in conjunction with the tner library.
 
## 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) and Bender et al. (2021)). 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'),

with a dictionary to map a label to its index ('label2id') as below.

 
 
 
 
## 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 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 presented in Lacoste et al. (2019).
 
- 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
 
BibTeX:
 

 
 
# 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>
 


</details>

# 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 
    - Associated Paper 
    - Space # Uses ## Direct Use
Token Classification ## Downstream Use [Optional]
 
This model can be used in conjunction with the tner library. ## 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) and Bender et al. (2021)). 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'),

with a dictionary to map a label to its index ('label2id') as below. ## 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 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 presented in Lacoste et al. (2019).
 
- 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
 
BibTeX: # 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>
 


</details>
TAGS
#transformers #pytorch #xlm-roberta #token-classification #en #arxiv-2209.12616 #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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 
    - Associated Paper 
    - Space # Uses ## Direct Use
Token Classification ## Downstream Use [Optional]
 
This model can be used in conjunction with the tner library. ## 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) and Bender et al. (2021)). 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'),

with a dictionary to map a label to its index ('label2id') as below. ## 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 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 presented in Lacoste et al. (2019).
 
- 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
 
BibTeX: # 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>
 


</details>
passage: TAGS
#transformers #pytorch #xlm-roberta #token-classification #en #arxiv-2209.12616 #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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 
    - Associated Paper 
    - Space# Uses## Direct Use
Token Classification## Downstream Use [Optional]
 
This model can be used in conjunction with the tner library.## 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) and Bender et al. (2021)). 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'),

with a dictionary to map a label to its index ('label2id') as below.## Training Procedure### Preprocessing
 
More information needed