Update README.md

README.md

@@ -1,18 +1,33 @@
 ---
 pipeline_tag: sentence-similarity
+language: fr
+datasets:
+- stsb_multi_mt
 tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
 - transformers
-
+license: mit
+model-index:
+- name: sentence-croissant-llm-base by Wissam Siblini
+  results:
+  - task:
+      name: Sentence-Embedding
+      type: Text Similarity
+    dataset:
+      name: Text Similarity fr
+      type: stsb_multi_mt
+      args: fr
+    metrics:
+    - name: Test Pearson correlation coefficient
+      type: Pearson_correlation_coefficient
+      value: xx.xx
 ---
 
-# Wissam42/sentence-croissant-llm-base
-
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 2048 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+# sentence-croissant-llm-base
 
-<!--- Describe your model here -->
+The model [sentence-croissant-llm-base](https://huggingface.co/Wissam42/sentence-croissant-llm-base) is designed to generate French text embeddings. It has been fine-tuned using the very recent pre-trained LLM [croissantllm/CroissantLLMBase](https://huggingface.co/croissantllm/CroissantLLMBase) with the strategy of Siamese-BERT implemented in the library ['sentences-transformers'](https://www.sbert.net/). The fine tuning dataset used is the French training split of [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/fr/train).
 
 ## Usage (Sentence-Transformers)
 
@@ -26,102 +41,24 @@ 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('Wissam42/sentence-croissant-llm-base')
+model =  SentenceTransformer("Wissam42/sentence-croissant-llm-base")
+sentences = ["Le chat mange la souris", "Un felin devore un rongeur", "Je travaille sur un ordinateur", "Je developpe sur mon pc"]
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
 
-
-
-## Usage (HuggingFace Transformers)
-Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
-
-```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)
-
-
-# Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
-
-# Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('Wissam42/sentence-croissant-llm-base')
-model = AutoModel.from_pretrained('Wissam42/sentence-croissant-llm-base')
-
-# Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
-
-# Compute token embeddings
-with torch.no_grad():
-    model_output = model(**encoded_input)
-
-# Perform pooling. In this case, mean pooling.
-sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-
-print("Sentence embeddings:")
-print(sentence_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=Wissam42/sentence-croissant-llm-base)
-
-
-## Training
-The model was trained with the parameters:
-
-**DataLoader**:
-
-`torch.utils.data.dataloader.DataLoader` of length 360 with parameters:
-```
-{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-
-**Loss**:
-
-`__main__.CosineSimilarityLoss` 
-
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 10,
-    "evaluation_steps": 500,
-    "evaluator": "__main__.CustomEmbeddingSimilarityEvaluator",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "eps": 1e-06,
-        "lr": 1e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 360,
-    "weight_decay": 0.01
-}
-```
-
-
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: LlamaModel 
-  (1): Pooling({'word_embedding_dimension': 2048, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
-)
-```
-
 ## Citing & Authors
 
-<!--- Describe where people can find more information -->
+	@article{faysse2024croissantllm,
+        title={CroissantLLM: A Truly Bilingual French-English Language Model},
+        author={Faysse, Manuel and Fernandes, Patrick and Guerreiro, Nuno and Loison, Ant{\'o}nio and Alves, Duarte and Corro, Caio and Boizard, Nicolas and Alves, Jo{\~a}o and Rei, Ricardo and Martins, Pedro and others},
+        journal={arXiv preprint arXiv:2402.00786},
+        year={2024}
+    }
+
+	@article{reimers2019sentence,
+	   title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
+	   author={Nils Reimers, Iryna Gurevych},
+	   journal={https://arxiv.org/abs/1908.10084},
+	   year={2019}
+	}