Upload pruned model

1_Pooling/config.json

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+{
+    "word_embedding_dimension": 384,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false
+}

README.md

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+---
+pipeline_tag: sentence-similarity
+language: ['tgl']
+license: mit
+tags:
+  - trimmed
+library_name: sentence-transformers
+base_model: intfloat/multilingual-e5-large
+base_model_relation: quantized
+datasets:
+  - Lumberjackk/fineweb-2-trimming
+---
+
+# multilingual-e5-large-['tgl']-32768
+This model is a 39.73% smaller version of [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) optimized for [16384] language via vocabulary size reduction using the [trimming](https://huggingface.co/blog/introduction-to-trimming) method.  
+This trimmed model should perform similarly to the original model with only 32,768 tokens and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in the selected languages were removed from the vocabulary.
+
+## Model Statistics
+| Metric | Original | Trimmed | Reduction |
+|--------|----------|---------|-----------|
+| **Vocabulary size** | 250,002 tokens | 32,768 tokens | **86.89%** |
+| **Model size** | 559,890,432 params | 337,442,816 params | **39.73%** |
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/613b0a62a14099d5afed7830/SfY_JwG6rAcflGEekK-QG.png)
+
+## Mining Dataset Statistics
+- **Number of texts used for mining**: 200,000 texts  
+- **Dataset**: [Lumberjackk/fineweb-2-trimming](https://huggingface.co/datasets/Lumberjackk/fineweb-2-trimming)
+
+## Usage
+```python
+from sentence_transformers import SentenceTransformer
+# Download from the 🤗 Hub
+model = SentenceTransformer("Lumberjackk/multilingual-e5-large-['tgl']-32768")
+# Run inference with queries and documents
+query = "My query"
+documents = [
+    "Chunk 1",
+    "Chunk 2",
+    "Chunk 3",
+]
+query_embeddings = model.encode_query(query)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings.shape)
+# Compute similarities to determine a ranking
+similarities = model.similarity(query_embeddings, document_embeddings)
+print(similarities)
+```
+
+## Citation
+
+#### Multilingual E5
+```
+@article{wang2024multilingual,
+  title={Multilingual E5 Text Embeddings: A Technical Report},
+  author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
+  journal={arXiv preprint arXiv:2402.05672},
+  year={2024}
+}
+```

config.json

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+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "is_decoder": false,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "tie_word_embeddings": true,
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "transformers_version": "5.3.0.dev0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 16384
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:42215042ab69041569a94a2727c9cb316bbdbb098b7f0553c0d8b7cc54970094
+size 111746040

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"
+    },
+    {
+      "idx": 2,
+      "name": "2",
+      "path": "2_Normalize",
+      "type": "sentence_transformers.models.Normalize"
+    }
+  ]

sentence_bert_config.json

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+{
+    "max_seq_length": 512,
+    "do_lower_case": false
+}

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "is_local": false,
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "<unk>"
+}