Wsl Reader Deberta V3 Base

Table of Contents

Model Overview

Meet the Word Sense Linking model! This model is designed to help computers understand the meaning of words in a sentence. You know how sometimes a word can have multiple meanings? Like “bank” can be a place where you keep your money, or the side of a river? This model helps figure out which meaning is the correct one.

How does it work?

The model is made up of two main parts: a retriever and a reader. The retriever looks up possible meanings of a word in a big dictionary called WordNet, while the reader looks at the sentence and tries to match the word to the correct meaning.

Tasks and rank

Natural Language Understanding 10897th

Named Entity Recognition (NER) 2933rd

Named Entity Disambiguation 335th

Information Extraction 1151st

Learn More About the Ranking System

Capabilities

The model can be used to:

• Identify the correct meaning of a word in a sentence
• Provide a list of possible meanings for a word
• Help computers understand the context of a sentence

Imagine you’re reading a sentence with the word “bank”. Are you talking about a financial institution or the side of a river? This model can figure that out.

Primary Tasks

The model’s main job is to identify and disambiguate spans of text to their most suitable senses from a reference inventory. In other words, it tries to match words with their correct meanings.

Strengths

The model is particularly good at:

• Retrieving relevant senses from a large lexical database of English (WordNet)
• Extracting spans from input text and linking them to the retrieved documents

Unique Features

The model is composed of two main components:

• A retriever that finds relevant senses from a senses inventory
• A reader that extracts spans from the input text and links them to the retrieved documents

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

Bert Base Uncased 1831 Uncased BERT model

Roberta Base 399 Masked language model

Galactica 120b 149 Scientific language model

Performance

But how well does it perform?

Speed

The model’s speed is quite impressive. It can process large amounts of text quickly and efficiently. For example, it can analyze a sentence like “Bus drivers drive busses for a living” in a matter of seconds.

Accuracy

But speed is not everything. The model’s accuracy is also crucial. According to the validation results, Word Sense Linking achieves a high accuracy of 74.4% in terms of F1 score. This is comparable to other models like ==BEM_HEU== and ==ConSeC_HEU==.

Efficiency

The model’s efficiency is also worth noting. It can handle complex tasks like word sense linking with ease. For instance, it can identify the correct sense of the word “bus” in the sentence “Bus drivers drive busses for a living” and link it to the correct sense in the WordNet inventory.

Examples

The bus driver drove the bus down the street. {'spans': [{'start': 0, 'end': 11, 'label': 'bus driver: someone who drives a bus', 'text': 'The bus driver'}, {'start': 12, 'end': 17, 'label': 'drive: operate or control a vehicle', 'text': 'drove'}, {'start': 18, 'end': 22, 'label': 'bus: a vehicle carrying many passengers; used for public transport', 'text': 'the bus'}]}

The driver was very tired after the long drive. {'spans': [{'start': 0, 'end': 6, 'label': 'driver: the operator of a motor vehicle', 'text': 'The driver'}, {'start': 7, 'end': 12, 'label': 'drive: operate or control a vehicle', 'text': 'drive'}]}

The living room was beautifully decorated. {'spans': [{'start': 0, 'end': 11, 'label': 'living: the financial means whereby one lives', 'text': 'The living'}, {'start': 12, 'end': 17, 'label': 'room: a space that is part of a building', 'text': 'room'}]}

Here’s an example code snippet:

from wsl import WSL
from wsl.inference.data.objects import WSLOutput

wsl_model = WSL.from_pretrained("Babelscape/wsl-base")
relik_out: WSLOutput = wsl_model("Bus drivers drive busses for a living.")

The output will be a WSLOutput object containing the extracted spans and candidate senses.

Limitations

The Word Sense Linking model is a powerful tool, but like any AI model, it has its limitations.

Limited Domain Knowledge

The model’s performance is based on its training data, which may not cover all possible domains or scenarios. For instance, if the model is trained on a dataset that focuses on general knowledge, it may not perform well on specialized domains like medicine or law.

Ambiguity and Context

Word sense linking can be a challenging task, especially when dealing with ambiguous words or phrases. The model may struggle to accurately disambiguate senses in cases where the context is unclear or the word has multiple related meanings.

Reliance on WordNet

The model relies on WordNet, a large lexical database of English, to provide sense keys. While WordNet is a comprehensive resource, it may not always be up-to-date or exhaustive. This could lead to limitations in the model’s ability to recognize and link senses.

Format

The Word Sense Linking model accepts input in the form of text sequences and outputs a WSLOutput object.

Architecture

The model is composed of two main components:

1. Retriever: Retrieves relevant senses from a senses inventory (e.g., WordNet).
2. Reader: Extracts spans from the input text and links them to the retrieved documents.

Data Formats

The model outputs a WSLOutput object, which contains the following information:

• text: The original input text.
• tokens: A list of tokens extracted from the input text.
• id: A unique identifier for the output.
• spans: A list of spans extracted from the input text, each with a start and end index, a label, and the corresponding text.
• candidates: A list of candidate senses for each span, each with a text, id, and metadata.