Update README.md

README.md

@@ -5,7 +5,6 @@ language:
 library_name: transformers
 tags:
 - linformer
-- legal
 - medical
 - RoBERTa
 - pytorch
@@ -29,7 +28,7 @@ Jargon is available in several versions with different context sizes and types o
 | jargon-general-legal                                                                |   jargon-general-base   |18GB Legal Corpus
 | [jargon-multidomain-base](https://huggingface.co/PantagrueLLM/jargon-multidomain-base) |   jargon-general-base   |Medical+Legal Corpora|
 | jargon-legal                                                                        |         scratch         |18GB Legal Corpus|
-| jargon-legal-4096                                                                   |         scratch         |18GB Legal Corpus|
+| [jargon-legal-4096](https://huggingface.co/PantagrueLLM/jargon-legal-4096)          |         scratch         |18GB Legal Corpus|
 | [jargon-biomed](https://huggingface.co/PantagrueLLM/jargon-biomed)                    |         scratch         |5.4GB Medical Corpus|
 | [jargon-biomed-4096](https://huggingface.co/PantagrueLLM/jargon-biomed-4096)          |         scratch         |5.4GB Medical Corpus|
 | [jargon-NACHOS](https://huggingface.co/PantagrueLLM/jargon-NACHOS)                    |         scratch         |[NACHOS](https://drbert.univ-avignon.fr/)|
@@ -40,18 +39,34 @@ Jargon is available in several versions with different context sizes and types o
 
 The Jargon models were evaluated on an range of specialized downstream tasks.
 
+## Biomedical Benchmark
+
+Results averaged across five funs with varying random seeds.
+
+| |[**FrenchMedMCQA**](https://huggingface.co/datasets/qanastek/frenchmedmcqa)|[**MQC**](https://aclanthology.org/2020.lrec-1.72/)|[**CAS-POS**](https://clementdalloux.fr/?page_id=28)|[**ESSAI-POS**](https://clementdalloux.fr/?page_id=28)|[**CAS-SG**](https://aclanthology.org/W18-5614/)|[**MEDLINE**](https://huggingface.co/datasets/mnaguib/QuaeroFrenchMed)|[**EMEA**](https://huggingface.co/datasets/mnaguib/QuaeroFrenchMed)|[**E3C-NER**](https://live.european-language-grid.eu/catalogue/corpus/7618)|[**CLISTER**](https://aclanthology.org/2022.lrec-1.459/)|
+|-------------------------|:-----------------------:|:-----------------------:|:--------------------:|:--------------------:|:--------------------:|:--------------------:|:--------------------:|:--------------------:|:--------------------:|
+| **Task Type**           | Sequence Classification | Sequence Classification | Token Classification | Token Classification | Token Classification | Token Classification | Token Classification | Token Classification |          STS         |
+| **Metric**              |           EMR           |         Accuracy        |       Macro-F1       |       Macro-F1       |      Weighted F1     |      Weighted F1     |      Weighted F1     |      Weighted F1     | Spearman Correlation |
+| jargon-general-base     |           12.9          |           76.7          |         96.6         |         96.0         |         69.4         |         81.7         |         96.5         |         91.9         |         78.0         |
+| jargon-biomed           |           15.3          |           91.1          |         96.5         |         95.6         |         75.1         |         83.7         |         96.5         |         93.5         |         74.6         |
+| jargon-biomed-4096      |           14.4          |           78.9          |         96.6         |         95.9         |         73.3         |         82.3         |         96.3         |         92.5         |         65.3         |
+| jargon-general-biomed   |           16.1          |           69.7          |         95.1         |         95.1         |         67.8         |         78.2         |         96.6         |         91.3         |         59.7         |
+| jargon-multidomain-base |           14.9          |           86.9          |         96.3         |         96.0         |         70.6         |         82.4         |         96.6         |         92.6         |         74.8         |
+| jargon-NACHOS           |           13.3          |           90.7          |         96.3         |         96.2         |         75.0         |         83.4         |         96.8         |         93.1         |         70.9         |
+| jargon-NACHOS-4096      |           18.4          |           93.2          |         96.2         |         95.9         |         74.9         |         83.8         |         96.8         |         93.2         |         74.9         |
+
 For more info please check out the [paper](https://hal.science/hal-04535557/file/FB2_domaines_specialises_LREC_COLING24.pdf), accepted for publication at [LREC-COLING 2024](https://lrec-coling-2024.org/list-of-accepted-papers/).
 
 
 ## Using Jargon models with HuggingFace transformers
 
-You can get started with `jargon-general-biomed` using the code snippet below:
+You can get started with `jargon-NACHOS-4096` using the code snippet below:
 
 ```python
 from transformers import AutoModelForMaskedLM, AutoTokenizer, pipeline
 
-tokenizer = AutoTokenizer.from_pretrained("PantagrueLLM/jargon-general-biomed", trust_remote_code=True)
-model = AutoModelForMaskedLM.from_pretrained("PantagrueLLM/jargon-general-biomed", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("PantagrueLLM/jargon-NACHOS-4096", trust_remote_code=True)
+model = AutoModelForMaskedLM.from_pretrained("PantagrueLLM/jargon-NACHOS-4096", trust_remote_code=True)
 
 jargon_maskfiller = pipeline("fill-mask", model=model, tokenizer=tokenizer)
 output = jargon_maskfiller("Il est allé au <mask> hier")