Update README.md

README.md

@@ -1,89 +1,22 @@
 ---
+license: mit
 library_name: sentence-transformers
 pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
-
+- mteb
 ---
+# Squirtle
 
-# {MODEL_NAME}
-
-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.
-
-<!--- Describe your model here -->
-
-## Usage (Sentence-Transformers)
-
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
-
-```
-pip install -U sentence-transformers
-```
-
-Then you can use the model like this:
-
-```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
-
-model = SentenceTransformer('{MODEL_NAME}')
-embeddings = model.encode(sentences)
-print(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 113 with parameters:
-```
-{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-
-**Loss**:
-
-`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 
-
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 1,
-    "evaluation_steps": 1000,
-    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "lr": 2e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 0,
-    "weight_decay": 0.01
-}
-```
+Squirtle is a distill of [bge-base-en-v1.5](BAAI/bge-base-en-v1.5).
 
+## Intended purpose
 
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-  (2): Normalize()
-)
-```
+<span style="color:blue">This model is designed for use in semantic-autocomplete ([click here for demo](https://mihaiii.github.io/semantic-autocomplete/)).</span>
+Make sure you also pass `pipelineParams={{ pooling: "cls", normalize: true }}` since the default pooling in the component is mean.
 
-## Citing & Authors
+## Usage
 
-<!--- Describe where people can find more information -->
+Other than within [semantic-autocomplete](https://github.com/Mihaiii/semantic-autocomplete), you can use this model same as [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5#usage).