ONNX Model Zoo

Frenz/modelsent_test

By Frenz · ↓ 1 · ♥ 0 · Updated 2025-03-12

What's in this repo

This page lists the files in Frenz/modelsent_test, including sentiment-int8.onnx.

  • 1 ONNX models (.onnx files)
  • 17 files, including 5 .json, 2 .0, 1 .md, 1 .safetensors, 1 .2, 1 .3
  • Last updated 2025-03-12

ONNX files

File Size Download
sentiment-int8.onnx — Download

How to use

These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.

README

modelsent_test

This model is a fine-tuned version of albert/albert-base-v2 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2379
  • Accuracy: 0.9261
  • F1: 0.9261
  • Precision: 0.9261
  • Recall: 0.9261
  • Accuracy Label Negative: 0.9242
  • Accuracy Label Positive: 0.9278

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
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall Accuracy Label Negative Accuracy Label Positive
0.5403 0.2442 100 0.5274 0.7611 0.7596 0.7728 0.7611 0.8535 0.6746
0.2673 0.4884 200 0.2806 0.8980 0.8980 0.8994 0.8980 0.9230 0.8746
0.247 0.7326 300 0.2610 0.9029 0.9024 0.9074 0.9029 0.8434 0.9586
0.2357 0.9768 400 0.2560 0.9084 0.9084 0.9096 0.9084 0.9318 0.8864
0.2094 1.2198 500 0.3127 0.9090 0.9089 0.9123 0.9090 0.9508 0.8698
0.1695 1.4640 600 0.2298 0.9188 0.9187 0.9189 0.9188 0.9053 0.9314
0.2024 1.7082 700 0.2218 0.9206 0.9206 0.9214 0.9206 0.9394 0.9030
0.1155 1.9524 800 0.2061 0.9236 0.9236 0.9236 0.9236 0.9192 0.9278
0.1361 2.1954 900 0.2299 0.9218 0.9218 0.9226 0.9218 0.9407 0.9041
0.1235 2.4396 1000 0.2668 0.9212 0.9212 0.9246 0.9212 0.9634 0.8817
0.084 2.6838 1100 0.2733 0.9218 0.9218 0.9240 0.9218 0.9545 0.8911
0.1326 2.9280 1200 0.2395 0.9249 0.9249 0.9249 0.9249 0.9192 0.9302

Framework versions

  • Transformers 4.49.0
  • Pytorch 2.5.1+cu124
  • Datasets 3.3.2
  • Tokenizers 0.21.0

Model info

text-classification transformers
transformers tensorboard onnx safetensors albert text-classification generated_from_trainer base_model:albert/albert-base-v2 base_model:quantized:albert/albert-base-v2 license:apache-2.0 endpoints_compatible region:us
↓ 1 ♥ 0

Created: 2024-06-02

Updated: 2025-03-12

View on Hugging Face

Files (17)

.gitattributes
README.md
config.json
model.safetensors
runs/Dec19_20-58-22_bd1bb20444ea/events.out.tfevents.1734641916.bd1bb20444ea.1518.0
runs/Jun02_11-18-47_abf2682c2968/events.out.tfevents.1717327130.abf2682c2968.2623.2
runs/Jun02_11-18-47_abf2682c2968/events.out.tfevents.1717327818.abf2682c2968.2623.3
runs/Jun16_10-55-42_e074026fd21f/events.out.tfevents.1718535344.e074026fd21f.458.0
runs/Jun16_10-55-42_e074026fd21f/events.out.tfevents.1718536095.e074026fd21f.458.1
sentiment-int8.onnx ONNX
special_tokens_map.json
spiece.model
tokenizer.json
tokenizer_config.json
tokenizer_sentiment.pkl
trainer_state.json
training_args.bin

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