rishavranaut / FlanT5_Fact_U_lora

huggingface.co
Total runs: 4
24-hour runs: 0
7-day runs: 0
30-day runs: 0
Model's Last Updated: October 08 2024

Introduction of FlanT5_Fact_U_lora

Model Details of FlanT5_Fact_U_lora

FlanT5_Fact_U_lora

This model is a fine-tuned version of google/flan-t5-large on the None dataset. It achieves the following results on the evaluation set:

  • Accuracy: 0.7776
  • F1 score: 0.7675
  • Precision: 0.8410
  • Recall: 0.7059
  • Loss: 0.8427
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: 0.0001
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5
Training results
Training Loss Epoch Step Accuracy F1 score Precision Recall Validation Loss
0.6417 0.3138 2000 0.6635 0.6562 0.7 0.6176 0.6579
0.6567 0.6276 4000 0.6741 0.6706 0.7068 0.6380 0.9110
0.6682 0.9415 6000 0.7012 0.6895 0.75 0.6380 0.8755
0.6763 1.2553 8000 0.7341 0.7224 0.7903 0.6652 0.8310
0.6431 1.5691 10000 0.7447 0.7141 0.8549 0.6131 0.8857
0.6537 1.8829 12000 0.7541 0.7239 0.8698 0.6199 0.8334
0.637 2.1968 14000 0.7506 0.7268 0.8443 0.6380 0.8196
0.6097 2.5106 16000 0.7506 0.7112 0.8938 0.5905 1.0144
0.6288 2.8244 18000 0.7612 0.7421 0.8464 0.6606 0.7895
0.6178 3.1382 20000 0.7647 0.7567 0.8184 0.7036 0.8249
0.5761 3.4521 22000 0.7671 0.7574 0.8262 0.6991 0.7881
0.6056 3.7659 24000 0.7682 0.7559 0.8356 0.6900 0.7853
0.6314 4.0797 26000 0.7753 0.7674 0.8311 0.7127 0.7990
0.5668 4.3935 28000 0.7753 0.7591 0.8575 0.6810 0.8649
0.5822 4.7074 30000 0.7776 0.7675 0.8410 0.7059 0.8427
Framework versions
  • PEFT 0.11.1
  • Transformers 4.44.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1

Runs of rishavranaut FlanT5_Fact_U_lora on huggingface.co

4
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More Information About FlanT5_Fact_U_lora huggingface.co Model

More FlanT5_Fact_U_lora license Visit here:

https://choosealicense.com/licenses/apache-2.0

FlanT5_Fact_U_lora huggingface.co

FlanT5_Fact_U_lora huggingface.co is an AI model on huggingface.co that provides FlanT5_Fact_U_lora's model effect (), which can be used instantly with this rishavranaut FlanT5_Fact_U_lora model. huggingface.co supports a free trial of the FlanT5_Fact_U_lora model, and also provides paid use of the FlanT5_Fact_U_lora. Support call FlanT5_Fact_U_lora model through api, including Node.js, Python, http.

rishavranaut FlanT5_Fact_U_lora online free

FlanT5_Fact_U_lora huggingface.co is an online trial and call api platform, which integrates FlanT5_Fact_U_lora's modeling effects, including api services, and provides a free online trial of FlanT5_Fact_U_lora, you can try FlanT5_Fact_U_lora online for free by clicking the link below.

rishavranaut FlanT5_Fact_U_lora online free url in huggingface.co:

https://huggingface.co/rishavranaut/FlanT5_Fact_U_lora

FlanT5_Fact_U_lora install

FlanT5_Fact_U_lora is an open source model from GitHub that offers a free installation service, and any user can find FlanT5_Fact_U_lora on GitHub to install. At the same time, huggingface.co provides the effect of FlanT5_Fact_U_lora install, users can directly use FlanT5_Fact_U_lora installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

FlanT5_Fact_U_lora install url in huggingface.co:

https://huggingface.co/rishavranaut/FlanT5_Fact_U_lora

Url of FlanT5_Fact_U_lora

FlanT5_Fact_U_lora huggingface.co Url

Provider of FlanT5_Fact_U_lora huggingface.co

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