Average Model Cost: $0.0000
Number of Runs: 12,978
Models by this creator
The tner-xlm-roberta-large-all-english model is a token classification model that has been fine-tuned on Named Entity Recognition (NER) tasks using the XLM-RoBERTa architecture. It is designed to identify and classify named entities in English text.
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 Citation 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.