Update metadata manually

README.md

@@ -1,51 +1,51 @@
----
-pipeline_tag: sentence-similarity
-language: en
-license: apache-2.0
-tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-- transformers
----
-
-# sentence-transformers/sentence-t5-base
-
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
-
-This model was converted from the Tensorflow model [st5-base-1](https://tfhub.dev/google/sentence-t5/st5-base/1) to PyTorch. When using this model, have a look at the publication: [Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877). The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results.
-
-The model uses only the encoder from a T5-base model. The weights are stored in FP16.  
-
-
-## 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('sentence-transformers/sentence-t5-base')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-
-The model requires sentence-transformers version 2.2.0 or newer.
-
-## Evaluation Results
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/sentence-t5-base)
-
-
-
-## Citing & Authors
-
-If you find this model helpful, please cite the respective publication:
-[Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877)
+---
+language: en
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+pipeline_tag: sentence-similarity
+---
+
+# sentence-transformers/sentence-t5-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
+
+This model was converted from the Tensorflow model [st5-base-1](https://tfhub.dev/google/sentence-t5/st5-base/1) to PyTorch. When using this model, have a look at the publication: [Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877). The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results.
+
+The model uses only the encoder from a T5-base model. The weights are stored in FP16.  
+
+
+## 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('sentence-transformers/sentence-t5-base')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+The model requires sentence-transformers version 2.2.0 or newer.
+
+## Evaluation Results
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/sentence-t5-base)
+
+
+
+## Citing & Authors
+
+If you find this model helpful, please cite the respective publication:
+[Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877)