Update README.md

README.md

@@ -1,21 +1,23 @@
 ---
+license: mit
 library_name: sentence-transformers
 pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
-- transformers
-
+- gte
+- mteb
 ---
+# gte-micro
 
-# {MODEL_NAME}
+This is a distill of [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny).
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+## Intended purpose
 
-<!--- Describe your model here -->
+<span style="color:blue">This model is designed for use in semantic-autocomplete ([click here for demo](https://mihaiii.github.io/semantic-autocomplete/)).</span>
 
-## Usage (Sentence-Transformers)
+## Usage (Sentence-Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny))
 
 Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
@@ -29,14 +31,14 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('Mihaiii/gte-micro-v2')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
 
 
 
-## Usage (HuggingFace Transformers)
+## Usage (HuggingFace Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny))
 Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
 
 ```python
@@ -55,8 +57,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('Mihaiii/gte-micro-v2')
+model = AutoModel.from_pretrained('Mihaiii/gte-micro-v2')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -72,57 +74,5 @@ print("Sentence embeddings:")
 print(sentence_embeddings)
 ```
 
-
-
-## Evaluation Results
-
-<!--- Describe how your model was evaluated -->
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
-
-
-## Training
-The model was trained with the parameters:
-
-**DataLoader**:
-
-`torch.utils.data.dataloader.DataLoader` of length 137553 with parameters:
-```
-{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-
-**Loss**:
-
-`sentence_transformers.losses.MSELoss.MSELoss` 
-
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 1,
-    "evaluation_steps": 5000,
-    "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "eps": 1e-06,
-        "lr": 0.0001
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 1000,
-    "weight_decay": 0.01
-}
-```
-
-
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-)
-```
-
-## Citing & Authors
-
-<!--- Describe where people can find more information -->
+### Limitation (same as [gte-small](https://huggingface.co/thenlper/gte-small))
+This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.