Masioki commited on
Commit
a4eed7f
1 Parent(s): e610df6

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +28 -12
README.md CHANGED
@@ -3,7 +3,25 @@ tags:
3
  - generated_from_trainer
4
  model-index:
5
  - name: fusion_gttbsc_distilbert-uncased-best
6
- results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7
  ---
8
 
9
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -11,21 +29,22 @@ should probably proofread and complete it, then remove this comment. -->
11
 
12
  # fusion_gttbsc_distilbert-uncased-best
13
 
14
- This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
15
 
16
  ## Model description
17
 
18
- More information needed
 
 
 
 
 
19
 
20
- ## Intended uses & limitations
21
-
22
- More information needed
23
 
24
  ## Training and evaluation data
25
 
26
- More information needed
27
-
28
- ## Training procedure
29
 
30
  ### Training hyperparameters
31
 
@@ -41,9 +60,6 @@ The following hyperparameters were used during training:
41
  - num_epochs: 20
42
  - mixed_precision_training: Native AMP
43
 
44
- ### Training results
45
-
46
-
47
 
48
  ### Framework versions
49
 
 
3
  - generated_from_trainer
4
  model-index:
5
  - name: fusion_gttbsc_distilbert-uncased-best
6
+ results:
7
+ - task:
8
+ type: dialogue act classification
9
+ dataset:
10
+ name: asapp/slue-phase-2
11
+ type: hvb
12
+ metrics:
13
+ - name: F1 macro E2E
14
+ type: F1 macro
15
+ value: 71.72
16
+ - name: F1 macro GT
17
+ type: F1 macro
18
+ value: 73.48
19
+ datasets:
20
+ - asapp/slue-phase-2
21
+ language:
22
+ - en
23
+ metrics:
24
+ - f1-macro
25
  ---
26
 
27
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
29
 
30
  # fusion_gttbsc_distilbert-uncased-best
31
 
32
+ Ground truth text with prosody encoding and ASR encoding residual cross attention fusion multi-label DAC
33
 
34
  ## Model description
35
 
36
+ ASR encoder: [Whisper small](https://huggingface.co/openai/whisper-small) encoder
37
+ Prosody encoder: 2 layer transformer encoder with initial dense projection
38
+ Backbone: [DistilBert uncased](https://huggingface.co/distilbert/distilbert-base-uncased)
39
+ Fusion: 2 residual cross attention fusion layers (F_asr x F_text and F_prosody x F_text) with dense layer on top
40
+ Pooling: Self attention
41
+ Multi-label classification head: 2 dense layers with two dropouts 0.3 and Tanh activation inbetween
42
 
 
 
 
43
 
44
  ## Training and evaluation data
45
 
46
+ Trained on ground truth.
47
+ Evaluated on ground truth (GT) and normalized [Whisper small](https://huggingface.co/openai/whisper-small) transcripts (E2E).
 
48
 
49
  ### Training hyperparameters
50
 
 
60
  - num_epochs: 20
61
  - mixed_precision_training: Native AMP
62
 
 
 
 
63
 
64
  ### Framework versions
65