File size: 2,206 Bytes
f5ae8b2 54c4dd4 4e03af4 f5ae8b2 4e03af4 f5ae8b2 4e03af4 f5ae8b2 54c4dd4 f5ae8b2 54c4dd4 4e03af4 f5ae8b2 54c4dd4 4e03af4 f5ae8b2 54c4dd4 f5ae8b2 54c4dd4 f5ae8b2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: contract-ner-model-da
results: []
---
<!-- 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. -->
# contract-ner-model-da
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0026
- Micro F1: 0.9297
## 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: 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
|