models Jean Baptiste camembert ner

Jean-Baptiste-camembert-ner

Overview

Summary: camembert-ner is a NER model fine-tuned from camemBERT on the Wikiner-fr dataset and was validated on email/chat data. It shows better performance on entities that do not start with an uppercase. The model has four classes: O, MISC, PER, ORG and LOC. The model can be loaded using HuggingFace. The performance of the model is evaluated using seqeval. Overall, the model has precision 0.8859, recall 0.8971 and f1 0.8914. It shows good performance on PER entities, with precision, recall and f1 of 0.9372, 0.9598 and 0.9483 respectively. The model's author also provided a link to an article on how he used the model results to train a LSTM model for signature detection in emails.

The above summary was generated using ChatGPT. Review the original model card to understand the data used to train the model, evaluation metrics, license, intended uses, limitations and bias before using the model.

Inference samples

Inference type	Python sample (Notebook)	CLI with YAML
Real time	token-classification-online-endpoint.ipynb	token-classification-online-endpoint.sh
Batch	token-classification-batch-endpoint.ipynb	coming soon

Finetuning samples

Task	Use case	Dataset	Python sample (Notebook)	CLI with YAML
Text Classification	Emotion Detection	Emotion	emotion-detection.ipynb	emotion-detection.sh
Token Classification	Named Entity Recognition	Conll2003	named-entity-recognition.ipynb	named-entity-recognition.sh

Model Evaluation

Task	Use case	Dataset	Python sample (Notebook)	CLI with YAML
Token Classification	Token Classification	CoNLL 2003	evaluate-model-token-classification.ipynb	evaluate-model-token-classification.yml

Sample inputs and outputs (for real-time inference)

Sample input

{
    "inputs": {
        "input_string": ["Je m'appelle jean-baptiste et je vis à montréal", "george washington est allé à washington"]
    }
}

Sample output

[
    {
        "0": "['O', 'O', 'I-PER', 'O', 'O', 'O', 'O', 'I-LOC']"
    },
    {
        "0": "['I-PER', 'I-PER', 'O', 'O', 'O', 'I-LOC']"
    }
]

Version: 11

Tags

Preview computes_allow_list : ['Standard_NV12s_v3', 'Standard_NV24s_v3', 'Standard_NV48s_v3', 'Standard_NC6s_v3', 'Standard_NC12s_v3', 'Standard_NC24s_v3', 'Standard_NC24rs_v3', 'Standard_NC6s_v2', 'Standard_NC12s_v2', 'Standard_NC24s_v2', 'Standard_NC24rs_v2', 'Standard_NC4as_T4_v3', 'Standard_NC8as_T4_v3', 'Standard_NC16as_T4_v3', 'Standard_NC64as_T4_v3', 'Standard_ND6s', 'Standard_ND12s', 'Standard_ND24s', 'Standard_ND24rs', 'Standard_ND40rs_v2', 'Standard_ND96asr_v4'] license : mit model_specific_defaults : ordereddict([('apply_deepspeed', 'true'), ('apply_lora', 'true'), ('apply_ort', 'true')]) task : text-named-entity-recognition

View in Studio: https://ml.azure.com/registries/azureml/models/Jean-Baptiste-camembert-ner/version/11

License: mit

Properties

SHA: cc63721791a6e1d60f4764997bbb311667ec75d8

datasets: Jean-Baptiste/wikiner_fr

evaluation-min-sku-spec: 8|0|28|56

evaluation-recommended-sku: Standard_DS4_v2

finetune-min-sku-spec: 4|1|28|176

finetune-recommended-sku: Standard_NC24rs_v3

finetuning-tasks: text-classification, token-classification

inference-min-sku-spec: 8|0|28|56

inference-recommended-sku: Standard_DS4_v2, Standard_D8a_v4, Standard_D8as_v4, Standard_DS5_v2, Standard_D16a_v4, Standard_D16as_v4, Standard_D32a_v4, Standard_D32as_v4, Standard_D48a_v4, Standard_D48as_v4, Standard_D64a_v4, Standard_D64as_v4, Standard_D96a_v4, Standard_D96as_v4, Standard_FX12mds, Standard_F16s_v2, Standard_F32s_v2, Standard_F48s_v2, Standard_F64s_v2, Standard_F72s_v2, Standard_FX24mds, Standard_FX36mds, Standard_FX48mds, Standard_E8s_v3, Standard_E16s_v3, Standard_E32s_v3, Standard_E48s_v3, Standard_E64s_v3, Standard_NC4as_T4_v3, Standard_NC6s_v3, Standard_NC8as_T4_v3, Standard_NC12s_v3, Standard_NC16as_T4_v3, Standard_NC24s_v3, Standard_NC64as_T4_v3, Standard_NC24ads_A100_v4, Standard_NC48ads_A100_v4, Standard_NC96ads_A100_v4, Standard_ND96asr_v4, Standard_ND96amsr_A100_v4, Standard_ND40rs_v2

languages: fr