Better model performance

README.md

@@ -5,15 +5,10 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
-language:
-- pl
-license: lgpl-3.0
-library_name: sentence-transformers
-datasets:
-- radlab/polish-sts-dataset
+
 ---
 
-# polish-roberta-large-v2-sts
+# {MODEL_NAME}
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
@@ -31,9 +26,9 @@ Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["Ala ma kota", "Ala ma psa"]
+sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('radlab/polish-roberta-large-v2-sts')
+model = SentenceTransformer('{MODEL_NAME}')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -56,11 +51,11 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['Ala ma kota', 'Ala ma psa']
+sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('radlab/polish-roberta-large-v2-sts')
-model = AutoModel.from_pretrained('radlab/polish-roberta-large-v2-sts')
+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')
@@ -90,9 +85,9 @@ The model was trained with the parameters:
 
 **DataLoader**:
 
-`torch.utils.data.dataloader.DataLoader` of length 752 with parameters:
+`torch.utils.data.dataloader.DataLoader` of length 8225 with parameters:
 ```
-{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
 ```
 
 **Loss**:
@@ -103,7 +98,7 @@ Parameters of the fit()-Method:
 ```
 {
     "epochs": 5,
-    "evaluation_steps": 1000,
+    "evaluation_steps": 250,
     "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
     "max_grad_norm": 1,
     "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
@@ -112,7 +107,7 @@ Parameters of the fit()-Method:
     },
     "scheduler": "WarmupLinear",
     "steps_per_epoch": null,
-    "warmup_steps": 376,
+    "warmup_steps": 4113,
     "weight_decay": 0.01
 }
 ```

config.json

@@ -20,7 +20,7 @@
   "pad_token_id": 1,
   "position_embedding_type": "absolute",
   "torch_dtype": "float32",
-  "transformers_version": "4.25.0.dev0",
+  "transformers_version": "4.30.2",
   "type_vocab_size": 1,
   "use_cache": true,
   "vocab_size": 128001

config_sentence_transformers.json

@@ -1,7 +1,7 @@
 {
   "__version__": {
     "sentence_transformers": "2.2.2",
-    "transformers": "4.25.0.dev0",
-    "pytorch": "1.12.1+cu116"
+    "transformers": "4.30.2",
+    "pytorch": "2.0.1+cu117"
   }
 }

eval/similarity_evaluation_sts-dev_results.csv

@@ -1,6 +1,166 @@
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tokenizer_config.json

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 {
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