update model card README.md
Browse files
README.md
ADDED
@@ -0,0 +1,96 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
license: mit
|
3 |
+
tags:
|
4 |
+
- generated_from_trainer
|
5 |
+
model-index:
|
6 |
+
- name: predict-perception-bert-cause-concept
|
7 |
+
results: []
|
8 |
+
---
|
9 |
+
|
10 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
11 |
+
should probably proofread and complete it, then remove this comment. -->
|
12 |
+
|
13 |
+
# predict-perception-bert-cause-concept
|
14 |
+
|
15 |
+
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
|
16 |
+
It achieves the following results on the evaluation set:
|
17 |
+
- Loss: 0.4044
|
18 |
+
- Rmse: 0.6076
|
19 |
+
- Rmse Cause::a Causata da un concetto astratto (es. gelosia): 0.6076
|
20 |
+
- Mae: 0.4548
|
21 |
+
- Mae Cause::a Causata da un concetto astratto (es. gelosia): 0.4548
|
22 |
+
- R2: 0.5463
|
23 |
+
- R2 Cause::a Causata da un concetto astratto (es. gelosia): 0.5463
|
24 |
+
- Cos: 0.2174
|
25 |
+
- Pair: 0.0
|
26 |
+
- Rank: 0.5
|
27 |
+
- Neighbors: 0.3931
|
28 |
+
- Rsa: nan
|
29 |
+
|
30 |
+
## Model description
|
31 |
+
|
32 |
+
More information needed
|
33 |
+
|
34 |
+
## Intended uses & limitations
|
35 |
+
|
36 |
+
More information needed
|
37 |
+
|
38 |
+
## Training and evaluation data
|
39 |
+
|
40 |
+
More information needed
|
41 |
+
|
42 |
+
## Training procedure
|
43 |
+
|
44 |
+
### Training hyperparameters
|
45 |
+
|
46 |
+
The following hyperparameters were used during training:
|
47 |
+
- learning_rate: 1e-05
|
48 |
+
- train_batch_size: 20
|
49 |
+
- eval_batch_size: 8
|
50 |
+
- seed: 1996
|
51 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
52 |
+
- lr_scheduler_type: linear
|
53 |
+
- num_epochs: 30
|
54 |
+
|
55 |
+
### Training results
|
56 |
+
|
57 |
+
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Cause::a Causata da un concetto astratto (es. gelosia) | Mae | Mae Cause::a Causata da un concetto astratto (es. gelosia) | R2 | R2 Cause::a Causata da un concetto astratto (es. gelosia) | Cos | Pair | Rank | Neighbors | Rsa |
|
58 |
+
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------------------------------------------------------:|:------:|:----------------------------------------------------------:|:-------:|:---------------------------------------------------------:|:------:|:----:|:----:|:---------:|:---:|
|
59 |
+
| 1.08 | 1.0 | 15 | 0.9520 | 0.9323 | 0.9323 | 0.6560 | 0.6560 | -0.0680 | -0.0680 | 0.0435 | 0.0 | 0.5 | 0.3188 | nan |
|
60 |
+
| 0.9974 | 2.0 | 30 | 0.8621 | 0.8872 | 0.8872 | 0.5962 | 0.5962 | 0.0328 | 0.0328 | 0.1304 | 0.0 | 0.5 | 0.4066 | nan |
|
61 |
+
| 0.9337 | 3.0 | 45 | 0.9223 | 0.9176 | 0.9176 | 0.6608 | 0.6608 | -0.0347 | -0.0347 | 0.2174 | 0.0 | 0.5 | 0.3632 | nan |
|
62 |
+
| 0.966 | 4.0 | 60 | 0.8273 | 0.8691 | 0.8691 | 0.5874 | 0.5874 | 0.0719 | 0.0719 | 0.2174 | 0.0 | 0.5 | 0.3754 | nan |
|
63 |
+
| 0.8683 | 5.0 | 75 | 0.8741 | 0.8933 | 0.8933 | 0.6136 | 0.6136 | 0.0193 | 0.0193 | 0.2174 | 0.0 | 0.5 | 0.3529 | nan |
|
64 |
+
| 0.8522 | 6.0 | 90 | 0.7781 | 0.8428 | 0.8428 | 0.5732 | 0.5732 | 0.1271 | 0.1271 | 0.2174 | 0.0 | 0.5 | 0.4152 | nan |
|
65 |
+
| 0.7968 | 7.0 | 105 | 0.7257 | 0.8139 | 0.8139 | 0.5519 | 0.5519 | 0.1859 | 0.1859 | 0.2174 | 0.0 | 0.5 | 0.4152 | nan |
|
66 |
+
| 0.7166 | 8.0 | 120 | 0.7122 | 0.8064 | 0.8064 | 0.5792 | 0.5792 | 0.2010 | 0.2010 | 0.1304 | 0.0 | 0.5 | 0.3955 | nan |
|
67 |
+
| 0.6246 | 9.0 | 135 | 0.6771 | 0.7862 | 0.7862 | 0.5701 | 0.5701 | 0.2403 | 0.2403 | 0.0435 | 0.0 | 0.5 | 0.3955 | nan |
|
68 |
+
| 0.5205 | 10.0 | 150 | 0.6704 | 0.7823 | 0.7823 | 0.5735 | 0.5735 | 0.2479 | 0.2479 | 0.3913 | 0.0 | 0.5 | 0.4847 | nan |
|
69 |
+
| 0.4182 | 11.0 | 165 | 0.6852 | 0.7909 | 0.7909 | 0.5987 | 0.5987 | 0.2313 | 0.2313 | 0.3913 | 0.0 | 0.5 | 0.4847 | nan |
|
70 |
+
| 0.3984 | 12.0 | 180 | 0.6106 | 0.7466 | 0.7466 | 0.5696 | 0.5696 | 0.3150 | 0.3150 | 0.0435 | 0.0 | 0.5 | 0.2935 | nan |
|
71 |
+
| 0.3138 | 13.0 | 195 | 0.5867 | 0.7318 | 0.7318 | 0.5209 | 0.5209 | 0.3418 | 0.3418 | 0.2174 | 0.0 | 0.5 | 0.3119 | nan |
|
72 |
+
| 0.2323 | 14.0 | 210 | 0.5120 | 0.6837 | 0.6837 | 0.5007 | 0.5007 | 0.4256 | 0.4256 | 0.3043 | 0.0 | 0.5 | 0.3849 | nan |
|
73 |
+
| 0.2149 | 15.0 | 225 | 0.4789 | 0.6612 | 0.6612 | 0.4883 | 0.4883 | 0.4627 | 0.4627 | 0.3043 | 0.0 | 0.5 | 0.3849 | nan |
|
74 |
+
| 0.1753 | 16.0 | 240 | 0.4526 | 0.6428 | 0.6428 | 0.4775 | 0.4775 | 0.4922 | 0.4922 | 0.3043 | 0.0 | 0.5 | 0.3849 | nan |
|
75 |
+
| 0.1478 | 17.0 | 255 | 0.4383 | 0.6325 | 0.6325 | 0.4616 | 0.4616 | 0.5083 | 0.5083 | 0.2174 | 0.0 | 0.5 | 0.3931 | nan |
|
76 |
+
| 0.1289 | 18.0 | 270 | 0.4141 | 0.6148 | 0.6148 | 0.4478 | 0.4478 | 0.5355 | 0.5355 | 0.3043 | 0.0 | 0.5 | 0.3849 | nan |
|
77 |
+
| 0.1035 | 19.0 | 285 | 0.3952 | 0.6007 | 0.6007 | 0.4407 | 0.4407 | 0.5566 | 0.5566 | 0.3043 | 0.0 | 0.5 | 0.3849 | nan |
|
78 |
+
| 0.1087 | 20.0 | 300 | 0.4217 | 0.6205 | 0.6205 | 0.4505 | 0.4505 | 0.5269 | 0.5269 | 0.2174 | 0.0 | 0.5 | 0.3931 | nan |
|
79 |
+
| 0.1005 | 21.0 | 315 | 0.4065 | 0.6091 | 0.6091 | 0.4508 | 0.4508 | 0.5440 | 0.5440 | 0.2174 | 0.0 | 0.5 | 0.3931 | nan |
|
80 |
+
| 0.0868 | 22.0 | 330 | 0.3937 | 0.5995 | 0.5995 | 0.4470 | 0.4470 | 0.5584 | 0.5584 | 0.3043 | 0.0 | 0.5 | 0.3849 | nan |
|
81 |
+
| 0.0808 | 23.0 | 345 | 0.4132 | 0.6142 | 0.6142 | 0.4617 | 0.4617 | 0.5364 | 0.5364 | 0.2174 | 0.0 | 0.5 | 0.3931 | nan |
|
82 |
+
| 0.0737 | 24.0 | 360 | 0.4214 | 0.6203 | 0.6203 | 0.4659 | 0.4659 | 0.5272 | 0.5272 | 0.3043 | 0.0 | 0.5 | 0.4066 | nan |
|
83 |
+
| 0.0711 | 25.0 | 375 | 0.3863 | 0.5939 | 0.5939 | 0.4470 | 0.4470 | 0.5666 | 0.5666 | 0.3043 | 0.0 | 0.5 | 0.3849 | nan |
|
84 |
+
| 0.066 | 26.0 | 390 | 0.4353 | 0.6304 | 0.6304 | 0.4760 | 0.4760 | 0.5117 | 0.5117 | 0.2174 | 0.0 | 0.5 | 0.3931 | nan |
|
85 |
+
| 0.0681 | 27.0 | 405 | 0.4078 | 0.6101 | 0.6101 | 0.4612 | 0.4612 | 0.5426 | 0.5426 | 0.2174 | 0.0 | 0.5 | 0.3931 | nan |
|
86 |
+
| 0.0543 | 28.0 | 420 | 0.4118 | 0.6132 | 0.6132 | 0.4616 | 0.4616 | 0.5380 | 0.5380 | 0.2174 | 0.0 | 0.5 | 0.3931 | nan |
|
87 |
+
| 0.069 | 29.0 | 435 | 0.4041 | 0.6074 | 0.6074 | 0.4551 | 0.4551 | 0.5466 | 0.5466 | 0.2174 | 0.0 | 0.5 | 0.3931 | nan |
|
88 |
+
| 0.0604 | 30.0 | 450 | 0.4044 | 0.6076 | 0.6076 | 0.4548 | 0.4548 | 0.5463 | 0.5463 | 0.2174 | 0.0 | 0.5 | 0.3931 | nan |
|
89 |
+
|
90 |
+
|
91 |
+
### Framework versions
|
92 |
+
|
93 |
+
- Transformers 4.16.2
|
94 |
+
- Pytorch 1.10.2+cu113
|
95 |
+
- Datasets 1.18.3
|
96 |
+
- Tokenizers 0.11.0
|