File size: 6,437 Bytes
e303c00
186ce32
 
 
 
 
e303c00
186ce32
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9641f8d
186ce32
 
 
 
 
 
 
 
 
 
 
e303c00
186ce32
 
 
82b341e
 
 
 
 
 
 
ed836ed
82b341e
 
ed836ed
82b341e
 
 
 
 
 
 
 
 
 
 
ed836ed
 
 
186ce32
 
82b341e
ed836ed
 
 
82b341e
ed836ed
 
 
 
 
 
 
 
5895776
ed836ed
 
 
 
 
82b341e
 
 
ed836ed
 
 
4c19a40
186ce32
82b341e
ed836ed
 
 
 
82b341e
ed836ed
 
 
 
0b30e60
186ce32
0b30e60
186ce32
 
 
 
 
 
 
 
ed836ed
82b341e
 
 
 
 
 
 
 
 
 
ed836ed
82b341e
ed836ed
 
82b341e
 
 
 
186ce32
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
82b341e
a21e513
 
 
 
 
 
 
 
 
bdded2c
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
---

language:

- ca

license: apache-2.0

tags:

- "catalan"

- "textual entailment"

- "teca"

- "CaText"

- "Catalan Textual Corpus"

datasets:

- "projecte-aina/teca"

metrics:

- "accuracy"


model-index:
- name: roberta-base-ca-v2-cased-te
  results:
  - task: 
      type: text-classification  # Required. Example: automatic-speech-recognition
    dataset:
      type:   projecte-aina/teca
      name: TECA
    metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8314
        
widget:

- text: "M'agrades. T'estimo." 

- text: "M'agrada el sol i la calor. A la Garrotxa plou molt."

- text: "El llibre va caure per la finestra. El llibre va sortir volant."

- text: "El meu aniversari és el 23 de maig. Faré anys a finals de maig."

---

# Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Textual Entailment.

<details>
<summary>Click to expand</summary>

- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#training)
  - [Training data](#training-data)
  - [Training procedure](#training-procedure)
- [Evaluation](#evaluation)
   - [Variable and metrics](#variable-and-metrics)
   - [Evaluation results](#evaluation-results)
- [Additional information](#additional-information)
  - [Author](#author)
  - [Contact information](#contact-information)
  - [Copyright](#copyright)
  - [Licensing information](#licensing-information)
  - [Funding](#funding)
  - [Citing information](#citing-information)
  - [Disclaimer](#disclaimer)
</details>

## Model description

The **roberta-base-ca-v2-cased-te** is a Textual Entailment (TE) model for the Catalan language fine-tuned from the [roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-ca-v2 model card for more details).

## Intended uses and limitations

**roberta-base-ca-v2-cased-te** model can be used to recognize Textual Entailment (TE). The model is limited by its training dataset and may not generalize well for all use cases.

## How to use

Here is how to use this model:

```python
from transformers import pipeline
from pprint import pprint

nlp = pipeline("text-classification", model="projecte-aina/roberta-base-ca-v2-cased-te")
example = "M'agrada el sol i la calor. </s></s> A la Garrotxa plou molt."

te_results = nlp(example)
pprint(te_results)
```

## Limitations and bias
At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.

## Training

### Training data
We used the TE dataset in Catalan called [TE-ca](https://huggingface.co/datasets/projecte-aina/teca) for training and evaluation.

### Training procedure
The model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set.

## Evaluation

### Variable and metrics

This model was finetuned maximizing accuracy.

## Evaluation results
We evaluated the roberta-base-ca-cased-te on the TE-ca test set against standard multilingual and monolingual baselines:

| Model        | TE-ca (Accuracy) | 
| ------------|:----|
| roberta-base-ca-v2-cased-te | **83.14** |
| BERTa       | 79.26 |
| mBERT       | 74.63 |
| XLM-RoBERTa | 33.30 |

For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club).


## Additional information

### Author
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])

### Contact information
For further information, send an email to [email protected]

### Copyright
Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center 

### Licensing information
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)

### Funding
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).

### Citation information
If you use any of these resources (datasets or models) in your work, please cite our latest paper:
```bibtex
@inproceedings{armengol-estape-etal-2021-multilingual,
    title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
    author = "Armengol-Estap{\'e}, Jordi  and
      Carrino, Casimiro Pio  and
      Rodriguez-Penagos, Carlos  and
      de Gibert Bonet, Ona  and
      Armentano-Oller, Carme  and
      Gonzalez-Agirre, Aitor  and
      Melero, Maite  and
      Villegas, Marta",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.437",
    doi = "10.18653/v1/2021.findings-acl.437",
    pages = "4933--4946",
}
```

### Disclaimer

<details>
<summary>Click to expand</summary>

The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.

When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.

In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.

</details>