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---
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: validation
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.6122448979591837
- name: Recall
type: recall
value: 0.430622009569378
- name: F1
type: f1
value: 0.5056179775280899
- name: Accuracy
type: accuracy
value: 0.9499141930973114
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ner_model
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2729
- Precision: 0.6122
- Recall: 0.4306
- F1: 0.5056
- Accuracy: 0.9499
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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