Description
General Text Embeddings (GTE) model. Towards General Text Embeddings with Multi-stage Contrastive Learning
The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including information retrieval, semantic textual similarity, text reranking, etc.
| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) |
|---|---|---|---|---|---|---|---|---|---|---|---|
| gte-large | 0.67 | 1024 | 512 | 63.13 | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 |
| gte-base | 0.22 | 768 | 512 | 62.39 | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 |
| e5-large-v2 | 1.34 | 1024 | 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 |
| e5-base-v2 | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 |
| gte-small | 0.07 | 384 | 512 | 61.36 | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 |
| text-embedding-ada-002 | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 |
| e5-small-v2 | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 |
| sentence-t5-xxl | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 |
| all-mpnet-base-v2 | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 |
| sgpt-bloom-7b1-msmarco | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 |
| all-MiniLM-L12-v2 | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 |
| all-MiniLM-L6-v2 | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 |
| contriever-base-msmarco | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 |
| sentence-t5-base | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 |
How to use
document = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = Tokenizer()\
.setInputCols(["document"])\
.setOutputCol("token")
embeddings = BertEmbeddings.pretrained("gte_small", "en")\
.setInputCols(["document", "token"])\
.setOutputCol("embeddings")
val document = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val embeddings = BertEmbeddings.pretrained("gte_small", "en")
.setInputCols("document", "token")
.setOutputCol("embeddings")
Model Information
| Model Name: | gte_small |
| Compatibility: | Spark NLP 5.0.2+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [embeddings] |
| Language: | en |
| Size: | 79.9 MB |
| Case sensitive: | true |
References
GTE models are from Dingkun Long