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2_Dense/pytorch_model.bin

@@ -1,3 +1,3 @@
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README.md

@@ -8,7 +8,7 @@ tags:
 
 ---
 
-# LaBSE-veps
+# {MODEL_NAME}
 
 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.
 
@@ -28,7 +28,7 @@ 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('Lynxpda/LaBSE-veps')
+model = SentenceTransformer('{MODEL_NAME}')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -47,9 +47,9 @@ The model was trained with the parameters:
 
 **DataLoader**:
 
-`torch.utils.data.dataloader.DataLoader` of length 1175 with parameters:
+`torch.utils.data.dataloader.DataLoader` of length 2424 with parameters:
 ```
-{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+{'batch_size': 192, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
 ```
 
 **Loss**:
@@ -63,16 +63,16 @@ Parameters of the fit()-Method:
 ```
 {
     "epochs": 5,
-    "evaluation_steps": 20,
-    "evaluator": "__main__.ChainScoreEvaluator",
+    "evaluation_steps": 50,
+    "evaluator": "NoneType",
     "max_grad_norm": 1,
     "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
     "optimizer_params": {
-        "lr": 1e-05
+        "lr": 2e-05
     },
     "scheduler": "warmupcosine",
     "steps_per_epoch": null,
-    "warmup_steps": 500,
+    "warmup_steps": 200,
     "weight_decay": 0.01
 }
 ```

model.safetensors

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