cryptoNER Open-Source Cryptocurrency Named Entity Recognition Model - Accurately Identify Token Symbols, Names, and Addresses

Cryptoner

Developed by covalenthq

A cryptocurrency named entity recognition model fine-tuned based on xlm-roberta-base, specializing in identifying cryptocurrency token symbols, names, and blockchain addresses.

Sequence Labeling

Transformers

Open Source License:MIT #Cryptocurrency Entity Recognition #High-precision NER #Multilingual Token Detection

Downloads 201

Release Time : 10/22/2023

Model Overview

This model is a named entity recognition (NER) model optimized for the cryptocurrency domain, capable of accurately identifying entities such as token symbols (TICKER SYMBOL), token names (NAME), and blockchain explorer addresses (blockscanner ADDRESS) in text.

Model Features

High-precision Cryptocurrency Entity Recognition

Achieves an F1 score of 0.9970 on the evaluation set, accurately identifying cryptocurrency-related entities.

Multilingual Support

Based on the XLM-RoBERTa architecture, it can process multilingual text.

Domain-specific Optimization

Specially optimized for the cryptocurrency domain, with training data including a large amount of cryptocurrency-related text.

Model Capabilities

Cryptocurrency Entity Recognition

Multilingual Text Processing

Named Entity Classification

Use Cases

Cryptocurrency Analysis

Social Media Monitoring

Analyze cryptocurrency projects mentioned on social media, identifying token symbols and names.

Can accurately identify token symbols such as '$PROPHET'

Blockchain Data Analysis

Process blockchain explorer data to identify relevant entities in addresses and transaction information.

Financial Text Processing

Cryptocurrency News Analysis

Extract information about cryptocurrency projects mentioned in news articles.

🚀 cryptoNER

A fine - tuned model for Named Entity Recognition (NER) in the cryptocurrency domain, based on xlm - roberta - base.

🚀 Quick Start

This model is a fine - tuned version of [xlm - roberta - base](https://huggingface.co/xlm - roberta - base) on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0058
F1: 0.9970

✨ Features

This model is a fine - tuned version of xlm - roberta - base, specializing in Named Entity Recognition (NER) within the cryptocurrency domain. It is optimized to recognize and classify entities such as cryptocurrency TICKER SYMBOL, NAME, and blockscanner ADDRESS within text.

📚 Documentation

Intended uses

Designed primarily for NER tasks in the cryptocurrency sector, this model excels in identifying and categorizing ticker symbol, token name, and blockscanner address in textual content.

Limitations

Performance may be subpar when the model encounters entities outside its training data or infrequently occurring entities within the cryptocurrency domain. The model might also be susceptible to variations in entity presentation and context.

Training and evaluation data

The model was trained using a diverse dataset, including artificially generated tweets and ERC20 token metadata fetched through the Covalent API (https://www.covalenthq.com/docs/unified - api/). GPT was employed to generate 500 synthetic tweets tailored for the cryptocurrency domain. The Covalent API was instrumental in obtaining a rich set of 20K+ unique ERC20 token metadata entries, enhancing the model's understanding and recognition of cryptocurrency entities.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e - 05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss	F1
0.0269	1.0	750	0.0080	0.9957
0.0049	2.0	1500	0.0074	0.9960
0.0042	3.0	2250	0.0074	0.9965
0.0034	4.0	3000	0.0058	0.9971
0.0028	5.0	3750	0.0059	0.9971
0.0024	6.0	4500	0.0058	0.9970

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1

📄 License

This project is licensed under the MIT license.

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