Downloads · 30 days
10
20% of all-time downloads
LLM_Teached_Bart_50k
GlycerinLOL/LLM_Teached_Bart_50k
LLM_Teached_Bart_50k is a machine learning model from GlycerinLOL. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
49
Public
Parameters
406M
6.5 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors1.6 GB · 100%
At a glance
- Library
- transformers
- License
- mit
- Model type
- bart
- Access
- Public
- Created
- Mar 1, 2024
- Updated
- Mar 1, 2024
- SHA
- 1eff9333
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
LLM_Teached_Bart_50k
This model is a fine-tuned version of facebook/bart-large-xsum on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.5590
- Rouge1: 0.4909
- Rouge2: 0.2303
- Rougel: 0.3967
- Rougelsum: 0.3965
- Gen Len: 38.2287
- Precision: 0.9063
- Recall: 0.9187
- F1: 0.9123
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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 390 | 1.6214 | 0.4804 | 0.2218 | 0.3873 | 0.3873 | 38.3549 | 0.9049 | 0.9166 | 0.9106 |
| 1.5842 | 2.0 | 781 | 1.5548 | 0.4874 | 0.2283 | 0.3945 | 0.3945 | 37.8604 | 0.9059 | 0.9171 | 0.9113 |
| 1.3014 | 3.0 | 1172 | 1.5461 | 0.49 | 0.2294 | 0.3975 | 0.3974 | 37.7564 | 0.9064 | 0.918 | 0.912 |
| 1.18 | 3.99 | 1560 | 1.5590 | 0.4909 | 0.2303 | 0.3967 | 0.3965 | 38.2287 | 0.9063 | 0.9187 | 0.9123 |
Framework versions
- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0
- Library
- transformers
- Type
- bart
- License
- mit
- Created
- Mar 1, 2024
- Updated
- Mar 1, 2024