tomaarsen HF Staff commited on
Commit
37b7dae
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1 Parent(s): 2a0ed92

Add new CrossEncoder model

Browse files
.gitattributes CHANGED
@@ -26,3 +26,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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  model.safetensors filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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  model.safetensors filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -1,63 +1,63 @@
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- ---
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- license: apache-2.0
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- language:
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- - en
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- - ar
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- - zh
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- - nl
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- - fr
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- - de
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- - hi
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- - in
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- - it
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- - ja
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- - pt
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- - ru
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- - es
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- - vi
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- - multilingual
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- datasets:
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- - unicamp-dl/mmarco
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- base_model:
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- - nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large
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- pipeline_tag: text-ranking
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- library_name: sentence-transformers
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- tags:
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- - transformers
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- ---
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- # Cross-Encoder for multilingual MS Marco
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-
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- This model was trained on the [MMARCO](https://hf.co/unicamp-dl/mmarco) dataset. It is a machine translated version of MS MARCO using Google Translate. It was translated to 14 languages. In our experiments, we observed that it performs also well for other languages.
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-
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- As a base model, we used the [multilingual MiniLMv2](https://huggingface.co/nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large) model.
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-
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- The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
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-
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- ## Usage with SentenceTransformers
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-
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- The usage becomes easy when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
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- ```python
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- from sentence_transformers import CrossEncoder
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- model = CrossEncoder('model_name')
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- scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
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- ```
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-
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-
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-
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-
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- ## Usage with Transformers
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-
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- ```python
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- from transformers import AutoTokenizer, AutoModelForSequenceClassification
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- import torch
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-
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- model = AutoModelForSequenceClassification.from_pretrained('model_name')
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- tokenizer = AutoTokenizer.from_pretrained('model_name')
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-
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- features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
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-
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- model.eval()
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- with torch.no_grad():
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- scores = model(**features).logits
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- print(scores)
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  ```
 
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+ ---
2
+ license: apache-2.0
3
+ language:
4
+ - en
5
+ - ar
6
+ - zh
7
+ - nl
8
+ - fr
9
+ - de
10
+ - hi
11
+ - in
12
+ - it
13
+ - ja
14
+ - pt
15
+ - ru
16
+ - es
17
+ - vi
18
+ - multilingual
19
+ datasets:
20
+ - unicamp-dl/mmarco
21
+ base_model:
22
+ - nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large
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+ pipeline_tag: text-ranking
24
+ library_name: sentence-transformers
25
+ tags:
26
+ - transformers
27
+ ---
28
+ # Cross-Encoder for multilingual MS Marco
29
+
30
+ This model was trained on the [MMARCO](https://hf.co/unicamp-dl/mmarco) dataset. It is a machine translated version of MS MARCO using Google Translate. It was translated to 14 languages. In our experiments, we observed that it performs also well for other languages.
31
+
32
+ As a base model, we used the [multilingual MiniLMv2](https://huggingface.co/nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large) model.
33
+
34
+ The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
35
+
36
+ ## Usage with SentenceTransformers
37
+
38
+ The usage becomes easy when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
39
+ ```python
40
+ from sentence_transformers import CrossEncoder
41
+ model = CrossEncoder('model_name')
42
+ scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
43
+ ```
44
+
45
+
46
+
47
+
48
+ ## Usage with Transformers
49
+
50
+ ```python
51
+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
52
+ import torch
53
+
54
+ model = AutoModelForSequenceClassification.from_pretrained('model_name')
55
+ tokenizer = AutoTokenizer.from_pretrained('model_name')
56
+
57
+ features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
58
+
59
+ model.eval()
60
+ with torch.no_grad():
61
+ scores = model(**features).logits
62
+ print(scores)
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  ```
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- "sbert_ce_default_activation_function": "torch.nn.modules.linear.Identity"
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- }
 
 
 
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