Duplicate from Lajavaness/sentence-camembert-base

Co-authored-by: Van Tuan DANG <dangvantuan@users.noreply.huggingface.co>

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1_Pooling/config.json

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+}

README.md

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+---
+pipeline_tag: sentence-similarity
+language: fr
+datasets:
+- stsb_multi_mt
+tags:
+- Text
+- Sentence Similarity
+- Sentence-Embedding
+- camembert-base
+license: apache-2.0
+model-index:
+- name: sentence-camembert-base by Van Tuan DANG
+  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: 86.88
+library_name: sentence-transformers
+---
+
+## Pre-trained sentence embedding models are the state-of-the-art of Sentence Embeddings for French.
+This model is improved from [dangvantuan/sentence-camembert-base](https://huggingface.co/dangvantuan/sentence-camembert-base) using fine-tuning with [Augmented SBERT](https://aclanthology.org/2021.naacl-main.28.pdf) on dataset [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/fr/train) along with Pair Sampling Strategies through 2 models [CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large) and [dangvantuan/sentence-camembert-large](https://huggingface.co/dangvantuan/sentence-camembert-large)
+## Usage
+The model can be used directly (without a language model) as follows:
+
+```python
+from sentence_transformers import SentenceTransformer
+model =  SentenceTransformer("Lajavaness/sentence-camembert-base")
+
+sentences = ["Un avion est en train de décoller.",
+          "Un homme joue d'une grande flûte.",
+          "Un homme étale du fromage râpé sur une pizza.",
+          "Une personne jette un chat au plafond.",
+          "Une personne est en train de plier un morceau de papier.",
+          ]
+
+embeddings = model.encode(sentences)
+```
+
+## Evaluation
+The model can be evaluated as follows on the French test data of stsb.
+
+```python
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.readers import InputExample
+from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
+from datasets import load_dataset
+def convert_dataset(dataset):
+    dataset_samples=[]
+    for df in dataset:
+        score = float(df['similarity_score'])/5.0  # Normalize score to range 0 ... 1
+        inp_example = InputExample(texts=[df['sentence1'], 
+                                    df['sentence2']], label=score)
+        dataset_samples.append(inp_example)
+    return dataset_samples
+
+# Loading the dataset for evaluation
+df_dev = load_dataset("stsb_multi_mt", name="fr", split="dev")
+df_test = load_dataset("stsb_multi_mt", name="fr", split="test")
+
+# Convert the dataset for evaluation
+
+# For Dev set:
+dev_samples = convert_dataset(df_dev)
+val_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='sts-dev')
+val_evaluator(model, output_path="./")
+
+# For Test set:
+test_samples = convert_dataset(df_test)
+test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name='sts-test')
+test_evaluator(model, output_path="./")
+```
+
+**Test Result**: 
+The performance is measured using Pearson and Spearman correlation on the sts-benchmark:
+- On dev
+
+
+| Model  | Pearson correlation | Spearman correlation  |  #params  |
+| ------------- | ------------- | ------------- |------------- |
+| [Lajavaness/sentence-camembert-base](https://huggingface.co/Lajavaness/sentence-camembert-base)| 86.88 |86.73 | 110M |
+| [dangvantuan/sentence-camembert-base](https://huggingface.co/dangvantuan/sentence-camembert-base)| 86.73 |86.54 | 110M |
+ [inokufu/flaubert-base-uncased-xnli-sts](https://huggingface.co/inokufu/flaubert-base-uncased-xnli-sts)| 85.85 |85.71 | 137M |
+| [distiluse-base-multilingual-cased](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased) | 79.22 | 79.16|135M |
+
+
+- On test: Pearson and Spearman correlation are evaluated on many different benchmarks dataset:
+
+**Pearson score**
+| Model                                                      | [STS-B](https://huggingface.co/datasets/stsb_multi_mt/viewer/fr/train)   | [STS12-fr ](https://huggingface.co/datasets/Lajavaness/STS12-fr)| [STS13-fr](https://huggingface.co/datasets/Lajavaness/STS13-fr) | [STS14-fr](https://huggingface.co/datasets/Lajavaness/STS14-fr) | [STS15-fr](https://huggingface.co/datasets/Lajavaness/STS15-fr) | [STS16-fr](https://huggingface.co/datasets/Lajavaness/STS16-fr) | [SICK-fr](https://huggingface.co/datasets/Lajavaness/SICK-fr) | params |
+|-----------------------------------------------------------|---------|----------|----------|----------|----------|----------|---------|--------|
+| [Lajavaness/sentence-camembert-base](https://huggingface.co/Lajavaness/sentence-camembert-base)                        | 83.46 | 84.49    | 84.61    | 83.94    | 86.94    | 75.20    | 82.86   | 110M   |
+| [inokufu/flaubert-base-uncased-xnli-sts](https://huggingface.co/inokufu/flaubert-base-uncased-xnli-sts)                    | 82.82 | 84.79    | 85.76    | 82.81    | 85.38    | 74.05    | 82.23   | 137M   |
+| [dangvantuan/sentence-camembert-base](https://huggingface.co/dangvantuan/sentence-camembert-base)                       | 82.36 | 82.06    | 84.08    | 81.51    | 85.54    | 73.97    | 80.91   | 110M   |
+| [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased)| 78.63 | 72.51    | 67.25    | 70.12    | 79.93    | 66.67    | 77.76   | 135M   |
+| [hugorosen/flaubert_base_uncased-xnli-sts](https://huggingface.co/hugorosen/flaubert_base_uncased-xnli-sts)                  | 78.38 | 79.00    | 77.61    | 76.56    | 79.03    | 71.22    | 80.58   | 137M   |
+| [antoinelouis/biencoder-camembert-base-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-camembert-base-mmarcoFR)            | 76.97 | 71.43    | 73.50    | 70.56    | 78.44    | 71.23    | 77.62   | 110M   |
+
+
+**Spearman score**
+| Model                                                      | [STS-B](https://huggingface.co/datasets/stsb_multi_mt/viewer/fr/train)   | [STS12-fr ](https://huggingface.co/datasets/Lajavaness/STS12-fr)| [STS13-fr](https://huggingface.co/datasets/Lajavaness/STS13-fr) | [STS14-fr](https://huggingface.co/datasets/Lajavaness/STS14-fr) | [STS15-fr](https://huggingface.co/datasets/Lajavaness/STS15-fr) | [STS16-fr](https://huggingface.co/datasets/Lajavaness/STS16-fr) | [SICK-fr](https://huggingface.co/datasets/Lajavaness/SICK-fr) | params |
+|-----------------------------------------------------------|---------|----------|----------|----------|----------|----------|---------|--------|
+| [Lajavaness/sentence-camembert-base](https://huggingface.co/Lajavaness/sentence-camembert-base)                         | 82.92 | 77.71    | 84.19    | 81.83    | 87.04    | 76.81    | 76.36   | 110M   |
+| [inokufu/flaubert-base-uncased-xnli-sts](https://huggingface.co/inokufu/flaubert-base-uncased-xnli-sts)                     | 83.07 | 77.34    | 85.88    | 80.96    | 85.70    | 76.43    | 77.00   | 137M   |
+| [dangvantuan/sentence-camembert-base](https://huggingface.co/dangvantuan/sentence-camembert-base)                        | 81.64 | 75.45    | 83.86    | 78.63    | 85.66    | 75.36    | 74.18   | 110M   |
+| [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased) | 77.49 | 69.80    | 68.85    | 68.17    | 80.27    | 70.04    | 72.49   | 135M   |
+| [hugorosen/flaubert_base_uncased-xnli-sts](https://huggingface.co/hugorosen/flaubert_base_uncased-xnli-sts)                   | 76.93 | 68.96    | 77.62    | 71.87    | 79.33    | 72.86    | 73.91   | 137M   |
+| [antoinelouis/biencoder-camembert-base-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-camembert-base-mmarcoFR)             | 75.55 | 66.89    | 73.90    | 67.14    | 78.78    | 72.64    | 72.03   | 110M   |
+
+
+## Citation
+
+
+	@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}
+	}
+
+
+	@article{martin2020camembert,
+	   title={CamemBERT: a Tasty French Language Mode},
+	   author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
+	   journal={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
+	   year={2020}
+	}
+    @article{thakur2020augmented,
+      title={Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks},
+      author={Thakur, Nandan and Reimers, Nils and Daxenberger, Johannes and Gurevych, Iryna},
+      journal={arXiv e-prints},
+      pages={arXiv--2010},
+      year={2020}

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config.json

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+  "architectures": [
+    "CamembertModel"
+  ],
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+    "pytorch": "1.9.1+cu102"
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+    "type": "sentence_transformers.models.Pooling"
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