Update README.md
#3
by
loic-dagnas-sinequa
- opened
README.md
CHANGED
|
@@ -1,18 +1,18 @@
|
|
| 1 |
---
|
| 2 |
pipeline_tag: sentence-similarity
|
| 3 |
tags:
|
| 4 |
-
|
| 5 |
-
|
| 6 |
language:
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
|
| 16 |
---
|
| 17 |
|
| 18 |
# Model Card for `vectorizer.raspberry`
|
|
@@ -27,15 +27,15 @@ Model name: `vectorizer.raspberry`
|
|
| 27 |
|
| 28 |
The model was trained and tested in the following languages:
|
| 29 |
|
| 30 |
-
- English
|
| 31 |
-
- French
|
| 32 |
- German
|
|
|
|
| 33 |
- Spanish
|
|
|
|
| 34 |
- Italian
|
| 35 |
- Dutch
|
| 36 |
- Japanese
|
| 37 |
- Portuguese
|
| 38 |
-
- Chinese
|
| 39 |
|
| 40 |
Besides these languages, basic support can be expected for additional 91 languages that were used during the pretraining
|
| 41 |
of the base model (see Appendix A of XLM-R paper).
|
|
@@ -115,10 +115,10 @@ We evaluated the model on the datasets of the [MIRACL benchmark](https://github.
|
|
| 115 |
multilingual capacities. Note that not all training languages are part of the benchmark, so we only report the metrics
|
| 116 |
for the existing languages.
|
| 117 |
|
| 118 |
-
| Language
|
| 119 |
-
|
| 120 |
-
|
|
| 121 |
-
|
|
| 122 |
-
|
|
| 123 |
-
| Japanese
|
| 124 |
-
| Chinese | 0.680 |
|
|
|
|
| 1 |
---
|
| 2 |
pipeline_tag: sentence-similarity
|
| 3 |
tags:
|
| 4 |
+
- feature-extraction
|
| 5 |
+
- sentence-similarity
|
| 6 |
language:
|
| 7 |
+
- de
|
| 8 |
+
- en
|
| 9 |
+
- es
|
| 10 |
+
- fr
|
| 11 |
+
- it
|
| 12 |
+
- nl
|
| 13 |
+
- ja
|
| 14 |
+
- pt
|
| 15 |
+
- zs
|
| 16 |
---
|
| 17 |
|
| 18 |
# Model Card for `vectorizer.raspberry`
|
|
|
|
| 27 |
|
| 28 |
The model was trained and tested in the following languages:
|
| 29 |
|
|
|
|
|
|
|
| 30 |
- German
|
| 31 |
+
- English
|
| 32 |
- Spanish
|
| 33 |
+
- French
|
| 34 |
- Italian
|
| 35 |
- Dutch
|
| 36 |
- Japanese
|
| 37 |
- Portuguese
|
| 38 |
+
- Simplified Chinese
|
| 39 |
|
| 40 |
Besides these languages, basic support can be expected for additional 91 languages that were used during the pretraining
|
| 41 |
of the base model (see Appendix A of XLM-R paper).
|
|
|
|
| 115 |
multilingual capacities. Note that not all training languages are part of the benchmark, so we only report the metrics
|
| 116 |
for the existing languages.
|
| 117 |
|
| 118 |
+
| Language | Recall@100 |
|
| 119 |
+
|:--------------------|-----------:|
|
| 120 |
+
| German | 0.528 |
|
| 121 |
+
| Spanish | 0.602 |
|
| 122 |
+
| French | 0.650 |
|
| 123 |
+
| Japanese | 0.614 |
|
| 124 |
+
| Simplified Chinese | 0.680 |
|