Update README.md

README.md

@@ -2598,11 +2598,72 @@ model-index:
       value: 78.07565728654365
 language:
 - en
-license: apache-2.0
+license: mit
 ---
 
 # gte-base
 
-Gegeral Text Embeddings (GTE) model.
- 
-This model has 12 layers and the embedding size is 768.
+Gegeral Text Embeddings (GTE) model. 
+
+The GTE series 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](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/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 series models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc.
+
+## Metrics
+
+We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
+
+| 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](https://huggingface.co/thenlper/gte-large) 	| 0.67 	| 1024 	| 512 	| 63.13 	| 46.84 	| 85 	| 59.13 	| 52.22 	| 83.35 	| 31.66 	| 73.33 	|
+| [gte-base](https://huggingface.co/thenlper/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](https://huggingface.co/intfloat/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](https://huggingface.co/intfloat/e5-base-v2) 	| 0.44 	| 768 	| 512 	| 61.5 	| 43.8 	| 85.73 	| 55.91 	| 50.29 	| 81.05 	| 30.28 	| 73.84 	|
+| [gte-small](https://huggingface.co/thenlper/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](https://platform.openai.com/docs/guides/embeddings) 	|  	| 1536 	| 8192 	| 60.99 	| 45.9 	| 84.89 	| 56.32 	| 49.25 	| 80.97 	| 30.8 	| 70.93 	|
+| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) 	| 0.13 	| 384 	| 512 	| 59.93 	| 39.92 	| 84.67 	| 54.32 	| 49.04 	| 80.39 	| 31.16 	| 72.94 	|
+| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/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](https://huggingface.co/sentence-transformers/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](https://huggingface.co/bigscience/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](https://huggingface.co/sentence-transformers/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](https://huggingface.co/sentence-transformers/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](https://huggingface.co/nthakur/contriever-base-msmarco) 	| 0.44 	| 768 	| 512 	| 56 	| 41.1 	| 82.54 	| 53.14 	| 41.88 	| 76.51 	| 30.36 	| 66.68 	|
+| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) 	| 0.22 	| 768 	| 512 	| 55.27 	| 40.21 	| 85.18 	| 53.09 	| 33.63 	| 81.14 	| 31.39 	| 69.81 	|
+
+## Usage
+
+Code example
+
+```
+import torch.nn.functional as F
+from torch import Tensor
+from transformers import AutoTokenizer, AutoModel
+
+def average_pool(last_hidden_states: Tensor,
+                 attention_mask: Tensor) -> Tensor:
+    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
+    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
+
+input_texts = [
+    "what is the capital of China?",
+    "how to implement quick sort in python?",
+    "Beijing",
+    "sorting algorithms"
+]
+
+tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-base")
+model = AutoModel.from_pretrained("thenlper/gte-base")
+
+# Tokenize the input texts
+batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
+
+outputs = model(**batch_dict)
+embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
+
+# (Optionally) normalize embeddings
+embeddings = F.normalize(embeddings, p=2, dim=1)
+scores = (embeddings[:1] @ embeddings[1:].T) * 100
+print(scores.tolist())
+```
+
+### Limitation
+
+This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.