model documentation (#2)

- model documentation (8f5a36dbfb5a272c98ae43de0a2b9e8a02924fc5)


Co-authored-by: Nazneen Rajani <nazneen@users.noreply.huggingface.co>

README.md

@@ -1,11 +1,199 @@
-# XLM-RoBERTa for NER
-XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
 
-## Usage
+---
+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>