Update README.md

README.md

@@ -4,13 +4,15 @@ tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
-
+language:
+- en
 ---
 
-# {MODEL_NAME}
+# all-mpnet-base-v2-eclass
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
+The model is based on the sentence-transformers/all-mpnet-base-v2 model and was fine-tuned with the [eclass-dataset](https://huggingface.co/datasets/gart-labor/eclassTrainST).
 <!--- Describe your model here -->
 
 ## Usage (Sentence-Transformers)
@@ -27,13 +29,11 @@ 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('gart-labor/all-mpnet-base-v2-eclass')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
 
-
-
 ## Evaluation Results
 
 <!--- Describe how your model was evaluated -->
@@ -41,42 +41,6 @@ print(embeddings)
 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 43680 with parameters:
-```
-{'batch_size': 16, '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": 2,
-    "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": 8736,
-    "weight_decay": 0.01
-}
-```
-
-
 ## Full Model Architecture
 ```
 SentenceTransformer(
@@ -84,8 +48,4 @@ SentenceTransformer(
   (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
   (2): Normalize()
 )
-```
-
-## Citing & Authors
-
-<!--- Describe where people can find more information -->
+```