Hugging Face's logo

answerdotai
/
answerai-colbert-small-v1

ONNX
Safetensors
English
bert
ColBERT
RAGatouille
passage-retrieval
Model card Files Community
15
bclavie commited on
Commit
f48715e
·
verified ·
1 Parent(s): 76cda8c

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +61 -47
README.md CHANGED
@@ -16,52 +16,6 @@ While being MiniLM-sized, it outperforms all previous similarly-sized models on
16
 
17
  For more information about this model or how it was trained, head over to the [announcement blogpost](https://www.answer.ai/posts/2024-08-13-small-but-mighty-colbert.html).
18
 
19
- ## Results
20
-
21
- ### Against single-vector models
22
-
23
- ![](https://www.answer.ai/posts/images/minicolbert/small_results.png)
24
-
25
-
26
- | Dataset / Model | answer-colbert-s | snowflake-s | bge-small-en | bge-base-en |
27
- |:-----------------|:-----------------:|:-------------:|:-------------:|:-------------:|
28
- | **Size** | 33M (1x) | 33M (1x) | 33M (1x) | **109M (3.3x)** |
29
- | **BEIR AVG** | **53.79** | 51.99 | 51.68 | 53.25 |
30
- | **FiQA2018** | **41.15** | 40.65 | 40.34 | 40.65 |
31
- | **HotpotQA** | **76.11** | 66.54 | 69.94 | 72.6 |
32
- | **MSMARCO** | **43.5** | 40.23 | 40.83 | 41.35 |
33
- | **NQ** | **59.1** | 50.9 | 50.18 | 54.15 |
34
- | **TRECCOVID** | **84.59** | 80.12 | 75.9 | 78.07 |
35
- | **ArguAna** | 50.09 | 57.59 | 59.55 | **63.61** |
36
- | **ClimateFEVER**| **33.07** | 35.2 | 31.84 | 31.17 |
37
- | **CQADupstackRetrieval** | 38.75 | 39.65 | 39.05 | **42.35** |
38
- | **DBPedia** | **45.58** | 41.02 | 40.03 | 40.77 |
39
- | **FEVER** | **90.96** | 87.13 | 86.64 | 86.29 |
40
- | **NFCorpus** | **37.3** | 34.92 | 34.3 | 37.39 |
41
- | **QuoraRetrieval** | 87.72 | 88.41 | **88.78** | 88.9 |
42
- | **SCIDOCS** | 18.42 | **21.82** | 20.52 | 21.73 |
43
- | **SciFact** | **74.77** | 72.22 | 71.28 | 74.04 |
44
- | **Touche2020** | 25.69 | 23.48 | **26.04** | 25.7 |
45
-
46
- ### Against ColBERTv2.0
47
-
48
- | Dataset / Model | answerai-colbert-small-v1 | ColBERTv2.0 |
49
- |:-----------------|:-----------------------:|:------------:|
50
- | **BEIR AVG** | **53.79** | 50.02 |
51
- | **DBPedia** | **45.58** | 44.6 |
52
- | **FiQA2018** | **41.15** | 35.6 |
53
- | **NQ** | **59.1** | 56.2 |
54
- | **HotpotQA** | **76.11** | 66.7 |
55
- | **NFCorpus** | **37.3** | 33.8 |
56
- | **TRECCOVID** | **84.59** | 73.3 |
57
- | **Touche2020** | 25.69 | **26.3** |
58
- | **ArguAna** | **50.09** | 46.3 |
59
- | **ClimateFEVER**| **33.07** | 17.6 |
60
- | **FEVER** | **90.96** | 78.5 |
61
- | **QuoraRetrieval** | **87.72** | 85.2 |
62
- | **SCIDOCS** | **18.42** | 15.4 |
63
- | **SciFact** | **74.77** | 69.3 |
64
-
65
  ## Usage
66
 
67
  ### Installation
@@ -93,6 +47,19 @@ ranker.rank(query=query, docs=docs)
93
 
94
  ### RAGatouille
95
 
 
 
 
 
 
 
 
 
 
 
 
 
 
96
  ### Stanford ColBERT
97
 
98
  #### Indexing
@@ -149,4 +116,51 @@ from colbert.modeling.checkpoint import Checkpoint
149
 
150
  ckpt = Checkpoint(answerdotai/answerai-colbert-small-v1", colbert_config=ColBERTConfig())
151
  embedded_query = ckpt.queryFromText(["Who dubs Howl's in English?"], bsize=16)
152
- ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
16
 
17
  For more information about this model or how it was trained, head over to the [announcement blogpost](https://www.answer.ai/posts/2024-08-13-small-but-mighty-colbert.html).
18
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
19
  ## Usage
20
 
21
  ### Installation
 
47
 
48
  ### RAGatouille
49
 
50
+ ```python
51
+ from ragatouille import RAGPretrainedModel
52
+
53
+ RAG = RAGPretrainedModel.from_pretrained("answerdotai/answerai-colbert-small-v1")
54
+
55
+ docs = ['Hayao Miyazaki is a Japanese director, born on [...]', 'Walt Disney is an American author, director and [...]', ...]
56
+
57
+ RAG.index(documents, index_name="ghibli")
58
+
59
+ query = 'Who directed spirited away?'
60
+ results = RAG.search(query)
61
+ ```
62
+
63
  ### Stanford ColBERT
64
 
65
  #### Indexing
 
116
 
117
  ckpt = Checkpoint(answerdotai/answerai-colbert-small-v1", colbert_config=ColBERTConfig())
118
  embedded_query = ckpt.queryFromText(["Who dubs Howl's in English?"], bsize=16)
119
+ ```
120
+
121
+
122
+ ## Results
123
+
124
+ ### Against single-vector models
125
+
126
+ ![](https://www.answer.ai/posts/images/minicolbert/small_results.png)
127
+
128
+
129
+ | Dataset / Model | answer-colbert-s | snowflake-s | bge-small-en | bge-base-en |
130
+ |:-----------------|:-----------------:|:-------------:|:-------------:|:-------------:|
131
+ | **Size** | 33M (1x) | 33M (1x) | 33M (1x) | **109M (3.3x)** |
132
+ | **BEIR AVG** | **53.79** | 51.99 | 51.68 | 53.25 |
133
+ | **FiQA2018** | **41.15** | 40.65 | 40.34 | 40.65 |
134
+ | **HotpotQA** | **76.11** | 66.54 | 69.94 | 72.6 |
135
+ | **MSMARCO** | **43.5** | 40.23 | 40.83 | 41.35 |
136
+ | **NQ** | **59.1** | 50.9 | 50.18 | 54.15 |
137
+ | **TRECCOVID** | **84.59** | 80.12 | 75.9 | 78.07 |
138
+ | **ArguAna** | 50.09 | 57.59 | 59.55 | **63.61** |
139
+ | **ClimateFEVER**| **33.07** | 35.2 | 31.84 | 31.17 |
140
+ | **CQADupstackRetrieval** | 38.75 | 39.65 | 39.05 | **42.35** |
141
+ | **DBPedia** | **45.58** | 41.02 | 40.03 | 40.77 |
142
+ | **FEVER** | **90.96** | 87.13 | 86.64 | 86.29 |
143
+ | **NFCorpus** | **37.3** | 34.92 | 34.3 | 37.39 |
144
+ | **QuoraRetrieval** | 87.72 | 88.41 | **88.78** | 88.9 |
145
+ | **SCIDOCS** | 18.42 | **21.82** | 20.52 | 21.73 |
146
+ | **SciFact** | **74.77** | 72.22 | 71.28 | 74.04 |
147
+ | **Touche2020** | 25.69 | 23.48 | **26.04** | 25.7 |
148
+
149
+ ### Against ColBERTv2.0
150
+
151
+ | Dataset / Model | answerai-colbert-small-v1 | ColBERTv2.0 |
152
+ |:-----------------|:-----------------------:|:------------:|
153
+ | **BEIR AVG** | **53.79** | 50.02 |
154
+ | **DBPedia** | **45.58** | 44.6 |
155
+ | **FiQA2018** | **41.15** | 35.6 |
156
+ | **NQ** | **59.1** | 56.2 |
157
+ | **HotpotQA** | **76.11** | 66.7 |
158
+ | **NFCorpus** | **37.3** | 33.8 |
159
+ | **TRECCOVID** | **84.59** | 73.3 |
160
+ | **Touche2020** | 25.69 | **26.3** |
161
+ | **ArguAna** | **50.09** | 46.3 |
162
+ | **ClimateFEVER**| **33.07** | 17.6 |
163
+ | **FEVER** | **90.96** | 78.5 |
164
+ | **QuoraRetrieval** | **87.72** | 85.2 |
165
+ | **SCIDOCS** | **18.42** | 15.4 |
166
+ | **SciFact** | **74.77** | 69.3 |