robingeibel commited on
Commit
2795b3e
1 Parent(s): cc452a8

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +65 -0
README.md ADDED
@@ -0,0 +1,65 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - generated_from_trainer
5
+ datasets:
6
+ - big_patent
7
+ model-index:
8
+ - name: led-base-16384-finetuned-big_patent
9
+ results: []
10
+ ---
11
+
12
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
13
+ should probably proofread and complete it, then remove this comment. -->
14
+
15
+ # led-base-16384-finetuned-big_patent
16
+
17
+ This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the big_patent dataset.
18
+ It achieves the following results on the evaluation set:
19
+ - Loss: 2.5094
20
+ - Rouge2 Precision: 0.128
21
+ - Rouge2 Recall: 0.1325
22
+ - Rouge2 Fmeasure: 0.125
23
+
24
+ ## Model description
25
+
26
+ More information needed
27
+
28
+ ## Intended uses & limitations
29
+
30
+ More information needed
31
+
32
+ ## Training and evaluation data
33
+
34
+ More information needed
35
+
36
+ ## Training procedure
37
+
38
+ ### Training hyperparameters
39
+
40
+ The following hyperparameters were used during training:
41
+ - learning_rate: 5e-05
42
+ - train_batch_size: 2
43
+ - eval_batch_size: 2
44
+ - seed: 42
45
+ - gradient_accumulation_steps: 4
46
+ - total_train_batch_size: 8
47
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
48
+ - lr_scheduler_type: linear
49
+ - num_epochs: 1
50
+ - mixed_precision_training: Native AMP
51
+
52
+ ### Training results
53
+
54
+ | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
55
+ |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
56
+ | 2.6657 | 0.4 | 500 | 2.6048 | 0.1211 | 0.131 | 0.121 |
57
+ | 2.6099 | 0.8 | 1000 | 2.5094 | 0.128 | 0.1325 | 0.125 |
58
+
59
+
60
+ ### Framework versions
61
+
62
+ - Transformers 4.19.3
63
+ - Pytorch 1.11.0+cu113
64
+ - Datasets 2.2.2
65
+ - Tokenizers 0.12.1