Update README.md

README.md

@@ -1,56 +1,66 @@
----
-pipeline_tag: sentence-similarity
-tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-
----
-
-# {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.
-
-<!--- Describe your model here -->
-
-## Usage (Sentence-Transformers)
-
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
-
-```
-pip install -U sentence-transformers
-```
-
-Then you can use the model like this:
-
-```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
-
-model = SentenceTransformer('{MODEL_NAME}')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-
-
-
-## Evaluation Results
-
-<!--- 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={MODEL_NAME})
-
-
-
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
-  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
-  (2): Normalize()
-)
-```
-
-## Citing & Authors
-
-<!--- Describe where people can find more information -->
+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+datasets:
+- HaifaCLGroup/KnessetCorpus
+language:
+- he
+base_model:
+- intfloat/multilingual-e5-large
+---
+
+# Knesset-multi-e5-large
+
+This is a [sentence-transformers](https://www.sbert.net) model. It maps sentences and paragraphs to a 1024-dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+This model is based on the [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) model. 
+The transformer encoder has been fine-tuned on [Knesset data](https://huggingface.co/datasets/HaifaCLGroup/KnessetCorpus) to better capture legislative and parliamentary language. 
+
+## Usage (Sentence-Transformers)
+
+Using this model is straightforward if you have [sentence-transformers](https://www.sbert.net) installed:
+
+```bash
+pip install -U sentence-transformers
+
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["זה משפט ראשון לדוגמה", "זה המשפט השני"]
+
+model = SentenceTransformer('Knesset-multi-e5-large')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+
+## Evaluation Results
+
+<!--- 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={MODEL_NAME})
+
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+  (2): Normalize()
+)
+```
+## Additional Details
+Base Model: intfloat/multilingual-e5-large
+Fine-Tuning Data: Knesset data
+Key Modifications:
+The encoder part has been fine-tuned on Knesset data to enhance performance for tasks involving legislative and parliamentary content. The original pooling and normalization layers have been retained to ensure that the model's embeddings remain consistent with the architecture of the base model.
+## Citing & Authors
+<!--- Describe where people can find more information -->
+TBD