Camembert-keyword-discriminator Open-source Text Keyword Classification Model - Accurately Identify Entity and Concept Keywords

Camembert Keyword Discriminator

Developed by yanekyuk

A text keyword classification model fine-tuned based on camembert-base, specialized in recognizing entity and concept keywords

Sequence Labeling

Transformers

Open Source License:MIT #French keyword recognition #Entity concept classification #High accuracy

Downloads 17

Release Time : 6/4/2022

Model Overview

This model is a fine-tuned version of camembert-base, specifically designed for keyword classification tasks, capable of distinguishing between entity and concept keywords in text. It demonstrates strong entity recognition capabilities in evaluations.

Model Features

Strong entity recognition capability

Achieves an F1 score of 0.7640 in entity recognition tasks, demonstrating excellent performance

Concept classification capability

Capable of distinguishing concept keywords with an F1 score of 0.5082

High accuracy

Overall accuracy reaches 93.44%, showing stable performance

Model Capabilities

Text keyword classification

Entity recognition

Concept classification

French text processing

Use Cases

Text analysis

Document keyword extraction

Automatically extracts important entity and concept keywords from French documents

Accurately identifies key information points in documents

Content classification

Classifies French content based on keywords

Improves content organization and management efficiency

Information retrieval

Search enhancement

Improves keyword matching capability for French search engines

Enhances search result relevance

🚀 camembert-keyword-discriminator

This model is a fine-tuned version of camembert-base on an unknown dataset. It can effectively perform keyword discrimination tasks, providing high - precision results in relevant evaluations.

🚀 Quick Start

This model is a fine - tuned version of camembert-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2180
Precision: 0.6646
Recall: 0.7047
Accuracy: 0.9344
F1: 0.6841
Ent/precision: 0.7185
Ent/accuracy: 0.8157
Ent/f1: 0.7640
Con/precision: 0.5324
Con/accuracy: 0.4860
Con/f1: 0.5082

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e - 05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
num_epochs: 8
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	Accuracy	F1	Ent/precision	Ent/accuracy	Ent/f1	Con/precision	Con/accuracy	Con/f1
0.2016	1.0	1875	0.1910	0.5947	0.7125	0.9243	0.6483	0.6372	0.8809	0.7395	0.4560	0.3806	0.4149
0.1454	2.0	3750	0.1632	0.6381	0.7056	0.9324	0.6701	0.6887	0.8291	0.7524	0.5064	0.4621	0.4833
0.1211	3.0	5625	0.1702	0.6703	0.6678	0.9343	0.6690	0.7120	0.7988	0.7529	0.5471	0.4094	0.4684
0.1021	4.0	7500	0.1745	0.6777	0.6708	0.9351	0.6742	0.7206	0.7956	0.7562	0.5557	0.4248	0.4815
0.0886	5.0	9375	0.1913	0.6540	0.7184	0.9340	0.6847	0.7022	0.8396	0.7648	0.5288	0.4795	0.5030
0.0781	6.0	11250	0.2021	0.6605	0.7132	0.9344	0.6858	0.7139	0.8258	0.7658	0.5293	0.4913	0.5096
0.0686	7.0	13125	0.2127	0.6539	0.7132	0.9337	0.6822	0.7170	0.8172	0.7638	0.5112	0.5083	0.5098
0.0667	8.0	15000	0.2180	0.6646	0.7047	0.9344	0.6841	0.7185	0.8157	0.7640	0.5324	0.4860	0.5082

Framework versions

Transformers 4.19.2
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1

📄 License

This project is under the MIT license.

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