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README.md

@@ -8,7 +8,7 @@ tags:
 
 ---
 
-# ellamind/e5small_sgb_aktg_bmf_experimental
+# {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.
 
@@ -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('ellamind/e5small_sgb_aktg_bmf_experimental')
+model = SentenceTransformer('{MODEL_NAME}')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -39,7 +39,7 @@ print(embeddings)
 
 <!--- 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=ellamind/e5small_sgb_aktg_bmf_experimental)
+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
@@ -47,9 +47,9 @@ The model was trained with the parameters:
 
 **DataLoader**:
 
-`torch.utils.data.dataloader.DataLoader` of length 1963 with parameters:
+`torch.utils.data.dataloader.DataLoader` of length 258 with parameters:
 ```
-{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
 ```
 
 **Loss**:
@@ -62,8 +62,8 @@ The model was trained with the parameters:
 Parameters of the fit()-Method:
 ```
 {
-    "epochs": 2,
-    "evaluation_steps": 50,
+    "epochs": 4,
+    "evaluation_steps": 200,
     "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
     "max_grad_norm": 1,
     "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
@@ -72,7 +72,7 @@ Parameters of the fit()-Method:
     },
     "scheduler": "WarmupLinear",
     "steps_per_epoch": null,
-    "warmup_steps": 392,
+    "warmup_steps": 103,
     "weight_decay": 0.01
 }
 ```

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "/workspace/models/embeddings/e5small_sgb_aktg_bmf",
+  "_name_or_path": "intfloat/multilingual-e5-small",
   "architectures": [
     "BertModel"
   ],

config_sentence_transformers.json

@@ -4,9 +4,6 @@
     "transformers": "4.39.3",
     "pytorch": "2.2.2+cu121"
   },
-  "prompts": {
-    "query": "",
-    "text": ""
-  },
+  "prompts": {},
   "default_prompt_name": null
 }

eval/Information-Retrieval_evaluation_results.csv

@@ -0,0 +1,9 @@
+epoch,steps,cos_sim-Accuracy@1,cos_sim-Accuracy@3,cos_sim-Accuracy@5,cos_sim-Accuracy@10,cos_sim-Precision@1,cos_sim-Recall@1,cos_sim-Precision@3,cos_sim-Recall@3,cos_sim-Precision@5,cos_sim-Recall@5,cos_sim-Precision@10,cos_sim-Recall@10,cos_sim-MRR@10,cos_sim-NDCG@10,cos_sim-MAP@100,dot_score-Accuracy@1,dot_score-Accuracy@3,dot_score-Accuracy@5,dot_score-Accuracy@10,dot_score-Precision@1,dot_score-Recall@1,dot_score-Precision@3,dot_score-Recall@3,dot_score-Precision@5,dot_score-Recall@5,dot_score-Precision@10,dot_score-Recall@10,dot_score-MRR@10,dot_score-NDCG@10,dot_score-MAP@100
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model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:bd8d1881f6db267855c1cdc7b6846a8fe1125f33665b86b1f3b50c8d1c4b6242
+oid sha256:2d6dad2648ba71627e8748f28020924f1311665556ef05e90814a4bd06ec2348
 size 470637416

tokenizer_config.json

@@ -46,17 +46,10 @@
   "cls_token": "<s>",
   "eos_token": "</s>",
   "mask_token": "<mask>",
-  "max_length": 512,
   "model_max_length": 512,
-  "pad_to_multiple_of": null,
   "pad_token": "<pad>",
-  "pad_token_type_id": 0,
-  "padding_side": "right",
   "sep_token": "</s>",
   "sp_model_kwargs": {},
-  "stride": 0,
   "tokenizer_class": "XLMRobertaTokenizer",
-  "truncation_side": "right",
-  "truncation_strategy": "longest_first",
   "unk_token": "<unk>"
 }