Update README.md

README.md

@@ -4,14 +4,18 @@ tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
-
+license: mit
+datasets:
+- sentence-transformers/embedding-training-data
+- clips/mfaq
+language:
+- da
+library_name: sentence-transformers
 ---
 
-# {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.
+# MiniLM-L6-danish-encoder 
 
-<!--- Describe your model here -->
+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. Maximum sequence length is 256 tokens.
 
 ## Usage (Sentence-Transformers)
 
@@ -27,56 +31,15 @@ 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('KennethTM/MiniLM-L6-danish-encoder')
 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 16542 with parameters:
-```
-{'batch_size': 24, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-
-**Loss**:
-
-`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
-  ```
-  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
-  ```
-
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 3,
-    "evaluation_steps": 0,
-    "evaluator": "NoneType",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "lr": 2e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 4963,
-    "weight_decay": 0.01
-}
-```
-
-
 ## Full Model Architecture
 ```
 SentenceTransformer(
@@ -85,7 +48,3 @@ SentenceTransformer(
   (2): Normalize()
 )
 ```
-
-## Citing & Authors
-
-<!--- Describe where people can find more information -->