Updating with freshly trained model

.gitattributes

@@ -29,3 +29,4 @@ pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
 .git/lfs/objects/60/ce/60ce79a96d7a41a207e79cfea9d5b62876b04591c19495178bedc5ff7c475311 filter=lfs diff=lfs merge=lfs -text
 .git/lfs/objects/f5/74/f5743ce637a953ee1776d97a870a26a646ffc332a295a4492acc2be2fe54daa3 filter=lfs diff=lfs merge=lfs -text
 .git/lfs/objects/32/58/32585d7934eb60f052d5f63f7c6ba6159183bccc30cde299785c25177df7c884 filter=lfs diff=lfs merge=lfs -text
+.git/lfs/objects/6c/33/6c338abd9c702b3be95cec15e23c96cf0d8c2e3b7c45df8d48de354f048e1d00 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -4,7 +4,6 @@ tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
-- transformers
 ---
 
 # {MODEL_NAME}
@@ -34,41 +33,6 @@ print(embeddings)
 
 
 
-## Usage (HuggingFace Transformers)
-Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
-
-```python
-from transformers import AutoTokenizer, AutoModel
-import torch
-
-
-def cls_pooling(model_output, attention_mask):
-    return model_output[0][:,0]
-
-
-# Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
-
-# Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
-
-# Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
-
-# Compute token embeddings
-with torch.no_grad():
-    model_output = model(**encoded_input)
-
-# Perform pooling. In this case, max pooling.
-sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
-
-print("Sentence embeddings:")
-print(sentence_embeddings)
-```
-
-
-
 ## Evaluation Results
 
 <!--- Describe how your model was evaluated -->
@@ -81,9 +45,9 @@ The model was trained with the parameters:
 
 **DataLoader**:
 
-`torch.utils.data.dataloader.DataLoader` of length 2841 with parameters:
+`torch.utils.data.dataloader.DataLoader` of length 14202 with parameters:
 ```
-{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
 ```
 
 **Loss**:
@@ -97,7 +61,7 @@ Parameters of the fit()-Method:
 ```
 {
     "callback": null,
-    "epochs": 5,
+    "epochs": 1,
     "evaluation_steps": 0,
     "evaluator": "NoneType",
     "max_grad_norm": 1,
@@ -115,9 +79,10 @@ Parameters of the fit()-Method:
 
 ## Full Model Architecture
 ```
-Codeformer(
+SentenceTransformer(
   (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel 
   (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+  (2): Normalize()
 )
 ```
 

modules.json

@@ -10,5 +10,11 @@
     "name": "1",
     "path": "1_Pooling",
     "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
   }
 ]

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6c338abd9c702b3be95cec15e23c96cf0d8c2e3b7c45df8d48de354f048e1d00
+oid sha256:9a5cefdac3bc73a3594dc32faf749f08773a4189f881d245531a9798d513125b
 size 498669047