Update README.md

README.md

@@ -78,16 +78,16 @@ widget:
     olmayan şeydir (teknoloji tutkunlarından ayrı olarak), yüksek volatilite dışında.
     Güvenilir bir işlem yeteneği tamamen eksikliği.'
 datasets:
-- selmanbaysan/msmarco-tr_fine_tuning_dataset
-- selmanbaysan/fiqa-tr_fine_tuning_dataset
-- selmanbaysan/scifact-tr_fine_tuning_dataset
-- selmanbaysan/nfcorpus-tr_fine_tuning_dataset
-- selmanbaysan/multinli_tr_fine_tuning_dataset
-- selmanbaysan/snli_tr_fine_tuning_dataset
-- selmanbaysan/stsb-tr
-- selmanbaysan/wmt16_en_tr_fine_tuning_dataset
-- selmanbaysan/quora-tr_fine_tuning_dataset
-- selmanbaysan/xnli_tr_fine_tuning_dataset
+- trmteb/msmarco-tr_fine_tuning_dataset
+- trmteb/fiqa-tr_fine_tuning_dataset
+- trmteb/scifact-tr_fine_tuning_dataset
+- trmteb/nfcorpus-tr_fine_tuning_dataset
+- trmteb/multinli_tr_fine_tuning_dataset
+- trmteb/snli_tr_fine_tuning_dataset
+- trmteb/stsb-tr
+- trmteb/wmt16_en_tr_fine_tuning_dataset
+- trmteb/quora-tr_fine_tuning_dataset
+- trmteb/xnli_tr_fine_tuning_dataset
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
 metrics:
@@ -338,7 +338,7 @@ model-index:
 
 # SentenceTransformer
 
-This is a [sentence-transformers](https://www.SBERT.net) model trained on the [msmarco-tr](https://huggingface.co/datasets/selmanbaysan/msmarco-tr_fine_tuning_dataset), [fiqa-tr](https://huggingface.co/datasets/selmanbaysan/fiqa-tr_fine_tuning_dataset), [scifact-tr](https://huggingface.co/datasets/selmanbaysan/scifact-tr_fine_tuning_dataset), [nfcorpus-tr](https://huggingface.co/datasets/selmanbaysan/nfcorpus-tr_fine_tuning_dataset), [multinli-tr](https://huggingface.co/datasets/selmanbaysan/multinli_tr_fine_tuning_dataset), [snli-tr](https://huggingface.co/datasets/selmanbaysan/snli_tr_fine_tuning_dataset), [stsb-tr](https://huggingface.co/datasets/selmanbaysan/stsb-tr) and [wmt16](https://huggingface.co/datasets/selmanbaysan/wmt16_en_tr_fine_tuning_dataset) datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+This is a [sentence-transformers](https://www.SBERT.net) model trained on the [msmarco-tr](https://huggingface.co/datasets/trmteb/msmarco-tr_fine_tuning_dataset), [fiqa-tr](https://huggingface.co/datasets/trmteb/fiqa-tr_fine_tuning_dataset), [scifact-tr](https://huggingface.co/datasets/trmteb/scifact-tr_fine_tuning_dataset), [nfcorpus-tr](https://huggingface.co/datasets/trmteb/nfcorpus-tr_fine_tuning_dataset), [multinli-tr](https://huggingface.co/datasets/trmteb/multinli_tr_fine_tuning_dataset), [snli-tr](https://huggingface.co/datasets/trmteb/snli_tr_fine_tuning_dataset), [stsb-tr](https://huggingface.co/datasets/trmteb/stsb-tr) and [wmt16](https://huggingface.co/datasets/trmteb/wmt16_en_tr_fine_tuning_dataset) datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
 
 ## Model Details
 
@@ -349,14 +349,14 @@ This is a [sentence-transformers](https://www.SBERT.net) model trained on the [m
 - **Output Dimensionality:** 768 dimensions
 - **Similarity Function:** Cosine Similarity
 - **Training Datasets:**
-    - [msmarco-tr](https://huggingface.co/datasets/selmanbaysan/msmarco-tr_fine_tuning_dataset)
-    - [fiqa-tr](https://huggingface.co/datasets/selmanbaysan/fiqa-tr_fine_tuning_dataset)
-    - [scifact-tr](https://huggingface.co/datasets/selmanbaysan/scifact-tr_fine_tuning_dataset)
-    - [nfcorpus-tr](https://huggingface.co/datasets/selmanbaysan/nfcorpus-tr_fine_tuning_dataset)
-    - [multinli-tr](https://huggingface.co/datasets/selmanbaysan/multinli_tr_fine_tuning_dataset)
-    - [snli-tr](https://huggingface.co/datasets/selmanbaysan/snli_tr_fine_tuning_dataset)
-    - [stsb-tr](https://huggingface.co/datasets/selmanbaysan/stsb-tr)
-    - [wmt16](https://huggingface.co/datasets/selmanbaysan/wmt16_en_tr_fine_tuning_dataset)
+    - [msmarco-tr](https://huggingface.co/datasets/trmteb/msmarco-tr_fine_tuning_dataset)
+    - [fiqa-tr](https://huggingface.co/datasets/trmteb/fiqa-tr_fine_tuning_dataset)
+    - [scifact-tr](https://huggingface.co/datasets/trmteb/scifact-tr_fine_tuning_dataset)
+    - [nfcorpus-tr](https://huggingface.co/datasets/trmteb/nfcorpus-tr_fine_tuning_dataset)
+    - [multinli-tr](https://huggingface.co/datasets/trmteb/multinli_tr_fine_tuning_dataset)
+    - [snli-tr](https://huggingface.co/datasets/trmteb/snli_tr_fine_tuning_dataset)
+    - [stsb-tr](https://huggingface.co/datasets/trmteb/stsb-tr)
+    - [wmt16](https://huggingface.co/datasets/trmteb/wmt16_en_tr_fine_tuning_dataset)
 <!-- - **Language:** Unknown -->
 <!-- - **License:** Unknown -->
 
@@ -390,7 +390,7 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("selmanbaysan/turkish_embedding_model_fine_tuned")
+model = SentenceTransformer("trmteb/turkish_embedding_model_fine_tuned")
 # Run inference
 sentences = [
     'Stoklara nasıl yatırım yapabilirim?',
@@ -480,7 +480,7 @@ You can finetune this model on your own dataset.
 
 #### msmarco-tr
 
-* Dataset: [msmarco-tr](https://huggingface.co/datasets/selmanbaysan/msmarco-tr_fine_tuning_dataset) at [f03d837](https://huggingface.co/datasets/selmanbaysan/msmarco-tr_fine_tuning_dataset/tree/f03d83704e5ea276665384ca6d8bee3b19632c80)
+* Dataset: [msmarco-tr](https://huggingface.co/datasets/trmteb/msmarco-tr_fine_tuning_dataset) at [f03d837](https://huggingface.co/datasets/trmteb/msmarco-tr_fine_tuning_dataset/tree/f03d83704e5ea276665384ca6d8bee3b19632c80)
 * Size: 253,304 training samples
 * Columns: <code>anchor</code> and <code>positive</code>
 * Approximate statistics based on the first 1000 samples:
@@ -506,7 +506,7 @@ You can finetune this model on your own dataset.
 
 #### fiqa-tr
 
-* Dataset: [fiqa-tr](https://huggingface.co/datasets/selmanbaysan/fiqa-tr_fine_tuning_dataset) at [bbc9e91](https://huggingface.co/datasets/selmanbaysan/fiqa-tr_fine_tuning_dataset/tree/bbc9e91b5710d0ac4032b5c9e94066470f928c8c)
+* Dataset: [fiqa-tr](https://huggingface.co/datasets/trmteb/fiqa-tr_fine_tuning_dataset) at [bbc9e91](https://huggingface.co/datasets/trmteb/fiqa-tr_fine_tuning_dataset/tree/bbc9e91b5710d0ac4032b5c9e94066470f928c8c)
 * Size: 14,166 training samples
 * Columns: <code>anchor</code> and <code>positive</code>
 * Approximate statistics based on the first 1000 samples:
@@ -532,7 +532,7 @@ You can finetune this model on your own dataset.
 
 #### scifact-tr
 
-* Dataset: [scifact-tr](https://huggingface.co/datasets/selmanbaysan/scifact-tr_fine_tuning_dataset) at [382de5b](https://huggingface.co/datasets/selmanbaysan/scifact-tr_fine_tuning_dataset/tree/382de5b316d8c8042a23f34179a73fadc13cb53d)
+* Dataset: [scifact-tr](https://huggingface.co/datasets/trmteb/scifact-tr_fine_tuning_dataset) at [382de5b](https://huggingface.co/datasets/trmteb/scifact-tr_fine_tuning_dataset/tree/382de5b316d8c8042a23f34179a73fadc13cb53d)
 * Size: 919 training samples
 * Columns: <code>anchor</code> and <code>positive</code>
 * Approximate statistics based on the first 919 samples:
@@ -558,7 +558,7 @@ You can finetune this model on your own dataset.
 
 #### nfcorpus-tr
 
-* Dataset: [nfcorpus-tr](https://huggingface.co/datasets/selmanbaysan/nfcorpus-tr_fine_tuning_dataset) at [22d1ef8](https://huggingface.co/datasets/selmanbaysan/nfcorpus-tr_fine_tuning_dataset/tree/22d1ef8b6a9f1c196d1977541a66ca8eff946f06)
+* Dataset: [nfcorpus-tr](https://huggingface.co/datasets/trmteb/nfcorpus-tr_fine_tuning_dataset) at [22d1ef8](https://huggingface.co/datasets/trmteb/nfcorpus-tr_fine_tuning_dataset/tree/22d1ef8b6a9f1c196d1977541a66ca8eff946f06)
 * Size: 110,575 training samples
 * Columns: <code>anchor</code> and <code>positive</code>
 * Approximate statistics based on the first 1000 samples:
@@ -584,7 +584,7 @@ You can finetune this model on your own dataset.
 
 #### multinli-tr
 
-* Dataset: [multinli-tr](https://huggingface.co/datasets/selmanbaysan/multinli_tr_fine_tuning_dataset) at [a700b72](https://huggingface.co/datasets/selmanbaysan/multinli_tr_fine_tuning_dataset/tree/a700b72da7056aa52ceb234d2e8a211d035dc2c7)
+* Dataset: [multinli-tr](https://huggingface.co/datasets/trmteb/multinli_tr_fine_tuning_dataset) at [a700b72](https://huggingface.co/datasets/trmteb/multinli_tr_fine_tuning_dataset/tree/a700b72da7056aa52ceb234d2e8a211d035dc2c7)
 * Size: 392,702 training samples
 * Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
 * Approximate statistics based on the first 1000 samples:
@@ -604,7 +604,7 @@ You can finetune this model on your own dataset.
 
 #### snli-tr
 
-* Dataset: [snli-tr](https://huggingface.co/datasets/selmanbaysan/snli_tr_fine_tuning_dataset) at [63eb107](https://huggingface.co/datasets/selmanbaysan/snli_tr_fine_tuning_dataset/tree/63eb107dfdaf0b16cfd209db25705f27f2e5e2ca)
+* Dataset: [snli-tr](https://huggingface.co/datasets/trmteb/snli_tr_fine_tuning_dataset) at [63eb107](https://huggingface.co/datasets/trmteb/snli_tr_fine_tuning_dataset/tree/63eb107dfdaf0b16cfd209db25705f27f2e5e2ca)
 * Size: 550,152 training samples
 * Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
 * Approximate statistics based on the first 1000 samples:
@@ -624,7 +624,7 @@ You can finetune this model on your own dataset.
 
 #### stsb-tr
 
-* Dataset: [stsb-tr](https://huggingface.co/datasets/selmanbaysan/stsb-tr) at [3d2e87d](https://huggingface.co/datasets/selmanbaysan/stsb-tr/tree/3d2e87d2a94c9af130b87ab8ed8d0c5c2e92e2df)
+* Dataset: [stsb-tr](https://huggingface.co/datasets/trmteb/stsb-tr) at [3d2e87d](https://huggingface.co/datasets/trmteb/stsb-tr/tree/3d2e87d2a94c9af130b87ab8ed8d0c5c2e92e2df)
 * Size: 5,740 training samples
 * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
 * Approximate statistics based on the first 1000 samples:
@@ -650,7 +650,7 @@ You can finetune this model on your own dataset.
 
 #### wmt16
 
-* Dataset: [wmt16](https://huggingface.co/datasets/selmanbaysan/wmt16_en_tr_fine_tuning_dataset) at [9fc4e73](https://huggingface.co/datasets/selmanbaysan/wmt16_en_tr_fine_tuning_dataset/tree/9fc4e7334bdb195b396c41eed05b0dd447981ef3)
+* Dataset: [wmt16](https://huggingface.co/datasets/trmteb/wmt16_en_tr_fine_tuning_dataset) at [9fc4e73](https://huggingface.co/datasets/trmteb/wmt16_en_tr_fine_tuning_dataset/tree/9fc4e7334bdb195b396c41eed05b0dd447981ef3)
 * Size: 205,756 training samples
 * Columns: <code>anchor</code> and <code>positive</code>
 * Approximate statistics based on the first 1000 samples:
@@ -678,7 +678,7 @@ You can finetune this model on your own dataset.
 
 #### msmarco-tr
 
-* Dataset: [msmarco-tr](https://huggingface.co/datasets/selmanbaysan/msmarco-tr_fine_tuning_dataset) at [f03d837](https://huggingface.co/datasets/selmanbaysan/msmarco-tr_fine_tuning_dataset/tree/f03d83704e5ea276665384ca6d8bee3b19632c80)
+* Dataset: [msmarco-tr](https://huggingface.co/datasets/trmteb/msmarco-tr_fine_tuning_dataset) at [f03d837](https://huggingface.co/datasets/trmteb/msmarco-tr_fine_tuning_dataset/tree/f03d83704e5ea276665384ca6d8bee3b19632c80)
 * Size: 31,538 evaluation samples
 * Columns: <code>anchor</code> and <code>positive</code>
 * Approximate statistics based on the first 1000 samples:
@@ -704,7 +704,7 @@ You can finetune this model on your own dataset.
 
 #### fiqa-tr
 
-* Dataset: [fiqa-tr](https://huggingface.co/datasets/selmanbaysan/fiqa-tr_fine_tuning_dataset) at [bbc9e91](https://huggingface.co/datasets/selmanbaysan/fiqa-tr_fine_tuning_dataset/tree/bbc9e91b5710d0ac4032b5c9e94066470f928c8c)
+* Dataset: [fiqa-tr](https://huggingface.co/datasets/trmteb/fiqa-tr_fine_tuning_dataset) at [bbc9e91](https://huggingface.co/datasets/trmteb/fiqa-tr_fine_tuning_dataset/tree/bbc9e91b5710d0ac4032b5c9e94066470f928c8c)
 * Size: 1,238 evaluation samples
 * Columns: <code>anchor</code> and <code>positive</code>
 * Approximate statistics based on the first 1000 samples:
@@ -730,7 +730,7 @@ You can finetune this model on your own dataset.
 
 #### quora-tr
 
-* Dataset: [quora-tr](https://huggingface.co/datasets/selmanbaysan/quora-tr_fine_tuning_dataset) at [6e1eee1](https://huggingface.co/datasets/selmanbaysan/quora-tr_fine_tuning_dataset/tree/6e1eee1e44db0f777eceb1f9b55293a9c2e25d76)
+* Dataset: [quora-tr](https://huggingface.co/datasets/trmteb/quora-tr_fine_tuning_dataset) at [6e1eee1](https://huggingface.co/datasets/trmteb/quora-tr_fine_tuning_dataset/tree/6e1eee1e44db0f777eceb1f9b55293a9c2e25d76)
 * Size: 7,626 evaluation samples
 * Columns: <code>anchor</code> and <code>positive</code>
 * Approximate statistics based on the first 1000 samples:
@@ -756,7 +756,7 @@ You can finetune this model on your own dataset.
 
 #### nfcorpus-tr
 
-* Dataset: [nfcorpus-tr](https://huggingface.co/datasets/selmanbaysan/nfcorpus-tr_fine_tuning_dataset) at [22d1ef8](https://huggingface.co/datasets/selmanbaysan/nfcorpus-tr_fine_tuning_dataset/tree/22d1ef8b6a9f1c196d1977541a66ca8eff946f06)
+* Dataset: [nfcorpus-tr](https://huggingface.co/datasets/trmteb/nfcorpus-tr_fine_tuning_dataset) at [22d1ef8](https://huggingface.co/datasets/trmteb/nfcorpus-tr_fine_tuning_dataset/tree/22d1ef8b6a9f1c196d1977541a66ca8eff946f06)
 * Size: 11,385 evaluation samples
 * Columns: <code>anchor</code> and <code>positive</code>
 * Approximate statistics based on the first 1000 samples:
@@ -782,7 +782,7 @@ You can finetune this model on your own dataset.
 
 #### snli-tr
 
-* Dataset: [snli-tr](https://huggingface.co/datasets/selmanbaysan/snli_tr_fine_tuning_dataset) at [63eb107](https://huggingface.co/datasets/selmanbaysan/snli_tr_fine_tuning_dataset/tree/63eb107dfdaf0b16cfd209db25705f27f2e5e2ca)
+* Dataset: [snli-tr](https://huggingface.co/datasets/trmteb/snli_tr_fine_tuning_dataset) at [63eb107](https://huggingface.co/datasets/trmteb/snli_tr_fine_tuning_dataset/tree/63eb107dfdaf0b16cfd209db25705f27f2e5e2ca)
 * Size: 10,000 evaluation samples
 * Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
 * Approximate statistics based on the first 1000 samples:
@@ -802,7 +802,7 @@ You can finetune this model on your own dataset.
 
 #### xnli-tr
 
-* Dataset: [xnli-tr](https://huggingface.co/datasets/selmanbaysan/xnli_tr_fine_tuning_dataset) at [3a66bc8](https://huggingface.co/datasets/selmanbaysan/xnli_tr_fine_tuning_dataset/tree/3a66bc878d3d027177da71f47e4d8dee21cafe63)
+* Dataset: [xnli-tr](https://huggingface.co/datasets/trmteb/xnli_tr_fine_tuning_dataset) at [3a66bc8](https://huggingface.co/datasets/trmteb/xnli_tr_fine_tuning_dataset/tree/3a66bc878d3d027177da71f47e4d8dee21cafe63)
 * Size: 2,490 evaluation samples
 * Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
 * Approximate statistics based on the first 1000 samples:
@@ -822,7 +822,7 @@ You can finetune this model on your own dataset.
 
 #### stsb-tr
 
-* Dataset: [stsb-tr](https://huggingface.co/datasets/selmanbaysan/stsb-tr) at [3d2e87d](https://huggingface.co/datasets/selmanbaysan/stsb-tr/tree/3d2e87d2a94c9af130b87ab8ed8d0c5c2e92e2df)
+* Dataset: [stsb-tr](https://huggingface.co/datasets/trmteb/stsb-tr) at [3d2e87d](https://huggingface.co/datasets/trmteb/stsb-tr/tree/3d2e87d2a94c9af130b87ab8ed8d0c5c2e92e2df)
 * Size: 1,496 evaluation samples
 * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
 * Approximate statistics based on the first 1000 samples:
@@ -848,7 +848,7 @@ You can finetune this model on your own dataset.
 
 #### wmt16
 
-* Dataset: [wmt16](https://huggingface.co/datasets/selmanbaysan/wmt16_en_tr_fine_tuning_dataset) at [9fc4e73](https://huggingface.co/datasets/selmanbaysan/wmt16_en_tr_fine_tuning_dataset/tree/9fc4e7334bdb195b396c41eed05b0dd447981ef3)
+* Dataset: [wmt16](https://huggingface.co/datasets/trmteb/wmt16_en_tr_fine_tuning_dataset) at [9fc4e73](https://huggingface.co/datasets/trmteb/wmt16_en_tr_fine_tuning_dataset/tree/9fc4e7334bdb195b396c41eed05b0dd447981ef3)
 * Size: 1,001 evaluation samples
 * Columns: <code>anchor</code> and <code>positive</code>
 * Approximate statistics based on the first 1000 samples: