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.ipynb_checkpoints/README-checkpoint.md

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+---
+license: cc-by-4.0
+language:
+- az
+metrics:
+- pearsonr
+base_model:
+- sentence-transformers/LaBSE
+pipeline_tag: sentence-similarity
+widget:
+- source_sentence: Bu xoşbəxt bir insandır
+  sentences:
+  - Bu xoşbəxt bir itdir
+  - Bu çox xoşbəxt bir insandır
+  - Bu gün günəşli bir gündür
+  example_title: Sentence Similarity
+tags:
+- labse
+---
+
+# TEmA-small
+
+This model is a fine-tuned version of the [LaBSE](https://huggingface.co/sentence-transformers/LaBSE), which is specialized for sentence similarity tasks in Azerbaijan texts. 
+It maps sentences and paragraphs to a 768-dimensional dense vector space, useful for tasks like clustering, semantic search, and more.
+
+
+
+
+## Benchmark Results
+
+| STSBenchmark | biosses-sts | sickr-sts | sts12-sts | sts13-sts | sts15-sts | sts16-sts | Average Pearson | Model                              |
+|--------------|-------------|-----------|-----------|-----------|-----------|-----------|-----------------|------------------------------------|
+| 0.8253       | 0.7859      | 0.7924    | 0.8444    | 0.7490    | 0.8141    | 0.7600    | 0.7959          | TEmA-small                         |
+| 0.7872       | 0.8303      | 0.7801    | 0.7978    | 0.6963    | 0.8052    | 0.7794    | 0.7823          | Cohere/embed-multilingual-v3.0     |
+| 0.7927       | 0.6672      | 0.7758    | 0.8122    | 0.7312    | 0.7831    | 0.7416    | 0.7577          | BAAI/bge-m3                        |
+| 0.7572       | 0.8139      | 0.7328    | 0.7646    | 0.6318    | 0.7542    | 0.7092    | 0.7377          | intfloat/multilingual-e5-large-instruct |
+| 0.7400       | 0.8216      | 0.6946    | 0.7098    | 0.6781    | 0.7637    | 0.7222    | 0.7329          | labse_stripped                     |
+| 0.7485       | 0.7714      | 0.7271    | 0.7170    | 0.6496    | 0.7570    | 0.7255    | 0.7280          | intfloat/multilingual-e5-large     |
+| 0.7245       | 0.8237      | 0.6839    | 0.6570    | 0.7125    | 0.7612    | 0.7386    | 0.7288          | OpenAI/text-embedding-3-large       |
+| 0.7363       | 0.8148      | 0.7067    | 0.7050    | 0.6535    | 0.7514    | 0.7070    | 0.7250          | sentence-transformers/LaBSE         |
+| 0.7376       | 0.7917      | 0.7190    | 0.7441    | 0.6286    | 0.7461    | 0.7026    | 0.7242          | intfloat/multilingual-e5-small     |
+| 0.7192       | 0.8198      | 0.7160    | 0.7338    | 0.5815    | 0.7318    | 0.6973    | 0.7142          | Cohere/embed-multilingual-light-v3.0 |
+| 0.6960       | 0.8185      | 0.6950    | 0.6752    | 0.5899    | 0.7186    | 0.6790    | 0.6960          | intfloat/multilingual-e5-base       |
+| 0.5830       | 0.2486      | 0.5921    | 0.5593    | 0.5559    | 0.5404    | 0.5289    | 0.5155          | antoinelouis/colbert-xm             |
+
+
+[STS-Benchmark](https://github.com/LocalDoc-Azerbaijan/STS-Benchmark)
+
+
+
+
+## Accuracy Results
+- **Cosine Distance:** 96.63
+- **Manhattan Distance:** 96.52
+- **Euclidean Distance:** 96.57
+
+
+
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+# Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+   token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+   input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+   return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+# Function to normalize embeddings
+def normalize_embeddings(embeddings):
+   return embeddings / embeddings.norm(dim=1, keepdim=True)
+
+# Sentences we want embeddings for
+sentences = [
+   "Bu xoşbəxt bir insandır",
+   "Bu çox xoşbəxt bir insandır", 
+   "Bu gün günəşli bir gündür"
+]
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('LocalDoc/TEmA-small')
+model = AutoModel.from_pretrained('LocalDoc/TEmA-small')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=128, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+   model_output = model(**encoded_input)
+
+# Perform pooling
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+# Normalize embeddings
+sentence_embeddings = normalize_embeddings(sentence_embeddings)
+
+# Calculate cosine similarities
+cosine_similarities = torch.nn.functional.cosine_similarity(
+   sentence_embeddings[0].unsqueeze(0), 
+   sentence_embeddings[1:], 
+   dim=1
+)
+
+print("Cosine Similarities:")
+for i, score in enumerate(cosine_similarities):
+   print(f"Sentence 1 <-> Sentence {i+2}: {score:.4f}")
+```
+

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

README.md

@@ -1,3 +1,111 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+language:
+- az
+metrics:
+- pearsonr
+base_model:
+- sentence-transformers/LaBSE
+pipeline_tag: sentence-similarity
+widget:
+- source_sentence: Bu xoşbəxt bir insandır
+  sentences:
+  - Bu xoşbəxt bir itdir
+  - Bu çox xoşbəxt bir insandır
+  - Bu gün günəşli bir gündür
+  example_title: Sentence Similarity
+tags:
+- labse
+---
+
+# TEmA-small
+
+This model is a fine-tuned version of the [LaBSE](https://huggingface.co/sentence-transformers/LaBSE), which is specialized for sentence similarity tasks in Azerbaijan texts. 
+It maps sentences and paragraphs to a 768-dimensional dense vector space, useful for tasks like clustering, semantic search, and more.
+
+
+
+
+## Benchmark Results
+
+| STSBenchmark | biosses-sts | sickr-sts | sts12-sts | sts13-sts | sts15-sts | sts16-sts | Average Pearson | Model                              |
+|--------------|-------------|-----------|-----------|-----------|-----------|-----------|-----------------|------------------------------------|
+| 0.8253       | 0.7859      | 0.7924    | 0.8444    | 0.7490    | 0.8141    | 0.7600    | 0.7959          | TEmA-small                         |
+| 0.7872       | 0.8303      | 0.7801    | 0.7978    | 0.6963    | 0.8052    | 0.7794    | 0.7823          | Cohere/embed-multilingual-v3.0     |
+| 0.7927       | 0.6672      | 0.7758    | 0.8122    | 0.7312    | 0.7831    | 0.7416    | 0.7577          | BAAI/bge-m3                        |
+| 0.7572       | 0.8139      | 0.7328    | 0.7646    | 0.6318    | 0.7542    | 0.7092    | 0.7377          | intfloat/multilingual-e5-large-instruct |
+| 0.7400       | 0.8216      | 0.6946    | 0.7098    | 0.6781    | 0.7637    | 0.7222    | 0.7329          | labse_stripped                     |
+| 0.7485       | 0.7714      | 0.7271    | 0.7170    | 0.6496    | 0.7570    | 0.7255    | 0.7280          | intfloat/multilingual-e5-large     |
+| 0.7245       | 0.8237      | 0.6839    | 0.6570    | 0.7125    | 0.7612    | 0.7386    | 0.7288          | OpenAI/text-embedding-3-large       |
+| 0.7363       | 0.8148      | 0.7067    | 0.7050    | 0.6535    | 0.7514    | 0.7070    | 0.7250          | sentence-transformers/LaBSE         |
+| 0.7376       | 0.7917      | 0.7190    | 0.7441    | 0.6286    | 0.7461    | 0.7026    | 0.7242          | intfloat/multilingual-e5-small     |
+| 0.7192       | 0.8198      | 0.7160    | 0.7338    | 0.5815    | 0.7318    | 0.6973    | 0.7142          | Cohere/embed-multilingual-light-v3.0 |
+| 0.6960       | 0.8185      | 0.6950    | 0.6752    | 0.5899    | 0.7186    | 0.6790    | 0.6960          | intfloat/multilingual-e5-base       |
+| 0.5830       | 0.2486      | 0.5921    | 0.5593    | 0.5559    | 0.5404    | 0.5289    | 0.5155          | antoinelouis/colbert-xm             |
+
+
+[STS-Benchmark](https://github.com/LocalDoc-Azerbaijan/STS-Benchmark)
+
+
+
+
+## Accuracy Results
+- **Cosine Distance:** 96.63
+- **Manhattan Distance:** 96.52
+- **Euclidean Distance:** 96.57
+
+
+
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+# Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+   token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+   input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+   return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+# Function to normalize embeddings
+def normalize_embeddings(embeddings):
+   return embeddings / embeddings.norm(dim=1, keepdim=True)
+
+# Sentences we want embeddings for
+sentences = [
+   "Bu xoşbəxt bir insandır",
+   "Bu çox xoşbəxt bir insandır", 
+   "Bu gün günəşli bir gündür"
+]
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('LocalDoc/TEmA-small')
+model = AutoModel.from_pretrained('LocalDoc/TEmA-small')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=128, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+   model_output = model(**encoded_input)
+
+# Perform pooling
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+# Normalize embeddings
+sentence_embeddings = normalize_embeddings(sentence_embeddings)
+
+# Calculate cosine similarities
+cosine_similarities = torch.nn.functional.cosine_similarity(
+   sentence_embeddings[0].unsqueeze(0), 
+   sentence_embeddings[1:], 
+   dim=1
+)
+
+print("Cosine Similarities:")
+for i, score in enumerate(cosine_similarities):
+   print(f"Sentence 1 <-> Sentence {i+2}: {score:.4f}")
+```
+

config.json

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+{
+  "_name_or_path": "/root/.cache/torch/sentence_transformers/LocalDoc_LaBSE-small-AZ",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.30.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 72164
+}

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.30.2",
+    "pytorch": "2.5.1+cu124"
+  }
+}

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e5a2034a34ddddc9b978c4d32a8b5d422f944f3486c9aa4c03711074cfc3ea2
+size 565924842

sentence_bert_config.json

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+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "rstrip": false,
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+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "full_tokenizer_file": null,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}