Instructions to use saattrupdan/employment-contract-ner-da with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saattrupdan/employment-contract-ner-da with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="saattrupdan/employment-contract-ner-da")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("saattrupdan/employment-contract-ner-da") model = AutoModelForTokenClassification.from_pretrained("saattrupdan/employment-contract-ner-da", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- da
license: mit
widget:
- >-
Medarbejderen starter arbejdet den 1. januar 2020 og afslutter arbejdet den
21. januar 2020. Den ugentlige arbejdstid er 37 timer, og medarbejderen
bliver aflønnet med 23.000,00 kr. om måneden. Arbejdsstedet er Supervej 21,
2000 Frederiksberg.
inference:
parameters:
aggregation_strategy: first
base_model: xlm-roberta-base
model-index:
- name: contract-ner-model-da
results: []
contract-ner-model-da
This model is a fine-tuned version of xlm-roberta-base on a custom contracts dataset. It achieves the following results on the evaluation set:
- Loss: 0.0026
- Micro F1: 0.9297
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 919
- num_epochs: 500
Training results
| Training Loss | Epoch | Step | Validation Loss | Micro F1 |
|---|---|---|---|---|
| 0.8971 | 0.24 | 200 | 0.0205 | 0.0 |
| 0.0173 | 0.48 | 400 | 0.0100 | 0.2921 |
| 0.0092 | 0.73 | 600 | 0.0065 | 0.7147 |
| 0.0063 | 0.97 | 800 | 0.0046 | 0.8332 |
| 0.0047 | 1.21 | 1000 | 0.0047 | 0.8459 |
| 0.0042 | 1.45 | 1200 | 0.0039 | 0.8694 |
| 0.0037 | 1.69 | 1400 | 0.0035 | 0.8888 |
| 0.0032 | 1.93 | 1600 | 0.0035 | 0.8840 |
| 0.0025 | 2.18 | 1800 | 0.0029 | 0.8943 |
| 0.0023 | 2.42 | 2000 | 0.0024 | 0.9104 |
| 0.0023 | 2.66 | 2200 | 0.0032 | 0.8808 |
| 0.0021 | 2.9 | 2400 | 0.0022 | 0.9338 |
| 0.0018 | 3.14 | 2600 | 0.0020 | 0.9315 |
| 0.0015 | 3.39 | 2800 | 0.0026 | 0.9297 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.8.1+cu101
- Datasets 1.12.1
- Tokenizers 0.10.3