falkne commited on
Commit
75a3bea
1 Parent(s): b55a643

Upload model

Browse files
README.md ADDED
@@ -0,0 +1,42 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - roberta
4
+ - adapterhub:argument/quality
5
+ - adapter-transformers
6
+ ---
7
+
8
+ # Adapter `falkne/effectiveness` for roberta-base
9
+
10
+ An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [argument/quality](https://adapterhub.ml/explore/argument/quality/) dataset and includes a prediction head for classification.
11
+
12
+ This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
13
+
14
+ ## Usage
15
+
16
+ First, install `adapter-transformers`:
17
+
18
+ ```
19
+ pip install -U adapter-transformers
20
+ ```
21
+ _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
22
+
23
+ Now, the adapter can be loaded and activated like this:
24
+
25
+ ```python
26
+ from transformers import AutoAdapterModel
27
+
28
+ model = AutoAdapterModel.from_pretrained("roberta-base")
29
+ adapter_name = model.load_adapter("falkne/effectiveness", source="hf", set_active=True)
30
+ ```
31
+
32
+ ## Architecture & Training
33
+
34
+ <!-- Add some description here -->
35
+
36
+ ## Evaluation results
37
+
38
+ <!-- Add some description here -->
39
+
40
+ ## Citation
41
+
42
+ <!-- Add some description here -->
adapter_config.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "config": {
3
+ "adapter_residual_before_ln": false,
4
+ "cross_adapter": false,
5
+ "factorized_phm_W": true,
6
+ "factorized_phm_rule": false,
7
+ "hypercomplex_nonlinearity": "glorot-uniform",
8
+ "init_weights": "bert",
9
+ "inv_adapter": null,
10
+ "inv_adapter_reduction_factor": null,
11
+ "is_parallel": false,
12
+ "learn_phm": true,
13
+ "leave_out": [],
14
+ "ln_after": false,
15
+ "ln_before": false,
16
+ "mh_adapter": false,
17
+ "non_linearity": "relu",
18
+ "original_ln_after": true,
19
+ "original_ln_before": true,
20
+ "output_adapter": true,
21
+ "phm_bias": true,
22
+ "phm_c_init": "normal",
23
+ "phm_dim": 4,
24
+ "phm_init_range": 0.0001,
25
+ "phm_layer": false,
26
+ "phm_rank": 1,
27
+ "reduction_factor": 16,
28
+ "residual_before_ln": true,
29
+ "scaling": 1.0,
30
+ "shared_W_phm": false,
31
+ "shared_phm_rule": true,
32
+ "use_gating": false
33
+ },
34
+ "hidden_size": 768,
35
+ "model_class": "RobertaAdapterModel",
36
+ "model_name": "roberta-base",
37
+ "model_type": "roberta",
38
+ "name": "effectiveness",
39
+ "version": "3.2.1"
40
+ }
head_config.json ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "config": {
3
+ "activation_function": "tanh",
4
+ "bias": true,
5
+ "head_type": "classification",
6
+ "label2id": {
7
+ "LABEL_0": 0
8
+ },
9
+ "layers": 2,
10
+ "num_labels": 1,
11
+ "use_pooler": false
12
+ },
13
+ "hidden_size": 768,
14
+ "model_class": "RobertaAdapterModel",
15
+ "model_name": "roberta-base",
16
+ "model_type": "roberta",
17
+ "name": "effectiveness",
18
+ "version": "3.2.1"
19
+ }
pytorch_adapter.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5aa80b8c5847fb5e133558aba73f5cb2d7b16675a86fe454c44addd627cab34e
3
+ size 3593877
pytorch_model_head.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:297e01650942f81392afb60659b744557050148ae5c8011054d1db9cbbe73453
3
+ size 2366879