rubert-base-srl-seqlabeling Open Source Model - Free Deployment for Identifying Predicates and Argument Roles in Russian Sentences

Rubert Base Srl Seqlabeling

Developed by Rexhaif

A Russian semantic role labeling model fine-tuned on ruBert-base, used to identify predicates and their related argument roles in sentences

Sequence Labeling

Transformers

#Russian Semantic Role Labeling #High-precision Predicate Recognition #Agent-Experiencer Analysis

Downloads 124

Release Time : 3/2/2022

Model Overview

This model is a sequence labeling model fine-tuned on ruBert-base, specifically designed for Russian Semantic Role Labeling (SRL) tasks. It can identify predicates in sentences and label semantic roles such as agents and experiencers.

Model Features

High-precision Semantic Role Recognition

Achieves high F1 scores (0.84-0.95) in agent and experiencer recognition

Multi-role Labeling Capability

Can simultaneously identify multiple semantic roles such as agents, experiencers, and instruments

Optimized Training Strategy

Uses cosine learning rate scheduling and warm-up strategies for stable training

Model Capabilities

Russian text analysis

Semantic role labeling

Predicate recognition

Argument role classification

Use Cases

Natural Language Processing

Russian Syntactic Analysis

Used for deep semantic analysis of Russian texts

Can accurately identify the doer and receiver of actions in sentences

Information Extraction System

Extracts structured event information from Russian texts

Can identify event participants and related instruments

🚀 rubert-base-srl-seqlabeling

This model is a fine - tuned version of ./ruBert-base/ on an unknown dataset. It achieves high - precision results in semantic role labeling tasks, with comprehensive evaluation metrics demonstrating its effectiveness.

🚀 Quick Start

This section provides a high - level overview of the model. The model is a fine - tuned version of ./ruBert-base/. Here are the evaluation results on the evaluation set:

Property	Details
Loss	0.1723
Causator Precision	0.8539
Causator Recall	0.8352
Causator F1	0.8444
Causator Number	91
Expiriencer Precision	0.9259
Expiriencer Recall	0.9740
Expiriencer F1	0.9494
Expiriencer Number	77
Instrument Precision	0.375
Instrument Recall	1.0
Instrument F1	0.5455
Instrument Number	3
Other Precision	0.0
Other Recall	0.0
Other F1	0.0
Other Number	1
Predicate Precision	0.9352
Predicate Recall	0.9902
Predicate F1	0.9619
Predicate Number	102
Overall Precision	0.8916
Overall Recall	0.9307
Overall F1	0.9107
Overall Accuracy	0.9667

📚 Documentation

Training and Evaluation Data

The documentation about training and evaluation data is not provided.

Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e - 05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.98) and epsilon = 1e - 06
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.06
num_epochs: 10.0

Training Results

Training Loss	Epoch	Step	Validation Loss	Causator Precision	Causator Recall	Causator F1	Causator Number	Expiriencer Precision	Expiriencer Recall	Expiriencer F1	Expiriencer Number	Instrument Precision	Instrument Recall	Instrument F1	Instrument Number	Other Number	Predicate Precision	Predicate Recall	Predicate F1	Predicate Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.2552	1.0	56	0.3471	0.8841	0.6703	0.7625	91	0.8421	0.8312	0.8366	77	0.0	0.0	0.0	3	1	0.9259	0.9804	0.9524	102	0.8893	0.8212	0.8539	0.9203
0.2385	2.0	112	0.1608	0.9103	0.7802	0.8402	91	0.9375	0.9740	0.9554	77	0.2857	0.6667	0.4	3	1	0.9519	0.9706	0.9612	102	0.9182	0.9015	0.9098	0.9554
0.0367	3.0	168	0.1311	0.8902	0.8022	0.8439	91	0.9375	0.9740	0.9554	77	0.4286	1.0	0.6	3	1	0.9709	0.9804	0.9756	102	0.9228	0.9161	0.9194	0.9673
0.0494	4.0	224	0.1507	0.7812	0.8242	0.8021	91	0.9241	0.9481	0.9359	77	0.4286	1.0	0.6	3	1	0.9524	0.9804	0.9662	102	0.8746	0.9161	0.8948	0.9637
0.0699	5.0	280	0.1830	0.8276	0.7912	0.8090	91	0.8941	0.9870	0.9383	77	0.375	1.0	0.5455	3	1	0.9352	0.9902	0.9619	102	0.875	0.9197	0.8968	0.9560
0.0352	6.0	336	0.1994	0.7857	0.8462	0.8148	91	0.9048	0.9870	0.9441	77	0.375	1.0	0.5455	3	1	0.9266	0.9902	0.9573	102	0.8595	0.9380	0.8970	0.9572
0.0186	7.0	392	0.1657	0.8652	0.8462	0.8556	91	0.9146	0.9740	0.9434	77	0.375	1.0	0.5455	3	1	0.9352	0.9902	0.9619	102	0.8920	0.9343	0.9127	0.9673
0.0052	8.0	448	0.1716	0.8556	0.8462	0.8508	91	0.9259	0.9740	0.9494	77	0.375	1.0	0.5455	3	1	0.9352	0.9902	0.9619	102	0.8920	0.9343	0.9127	0.9673
0.0094	9.0	504	0.1715	0.8444	0.8352	0.8398	91	0.9259	0.9740	0.9494	77	0.4286	1.0	0.6	3	1	0.9352	0.9902	0.9619	102	0.8916	0.9307	0.9107	0.9667
0.0078	10.0	560	0.1723	0.8539	0.8352	0.8444	91	0.9259	0.9740	0.9494	77	0.375	1.0	0.5455	3	1	0.9352	0.9902	0.9619	102	0.8916	0.9307	0.9107	0.9667

Framework Versions

Transformers 4.13.0.dev0
Pytorch 1.10.0+cu102
Datasets 1.15.1
Tokenizers 0.10.3

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