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---
language:
- en
license: mit
base_model: xlnet-base-cased
tags:
- low-resource NER
- token_classification
- biomedicine
- medical NER
- generated_from_trainer
datasets:
- medicine
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Dagobert42/xlnet-base-cased-biored-augmented
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. -->
# Dagobert42/xlnet-base-cased-biored-augmented
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the bigbio/biored dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1552
- Accuracy: 0.9545
- Precision: 0.8651
- Recall: 0.8306
- F1: 0.8454
- Weighted F1: 0.9544
## 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: 1.8e-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
- lr_scheduler_warmup_ratio: 0.004
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Weighted F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
| No log | 0.5 | 10 | 0.2276 | 0.9252 | 0.7871 | 0.7482 | 0.7616 | 0.9233 |
| No log | 1.0 | 20 | 0.2124 | 0.9318 | 0.8363 | 0.7571 | 0.7923 | 0.9298 |
| No log | 1.5 | 30 | 0.2052 | 0.9342 | 0.8199 | 0.794 | 0.8057 | 0.9334 |
| No log | 2.0 | 40 | 0.1958 | 0.9396 | 0.8132 | 0.8049 | 0.8038 | 0.9384 |
| No log | 2.5 | 50 | 0.2043 | 0.9385 | 0.8162 | 0.8086 | 0.811 | 0.9377 |
| No log | 3.0 | 60 | 0.1948 | 0.9409 | 0.8413 | 0.8109 | 0.8249 | 0.9404 |
| No log | 3.5 | 70 | 0.1951 | 0.9436 | 0.8449 | 0.7963 | 0.8186 | 0.9425 |
| No log | 4.0 | 80 | 0.2032 | 0.941 | 0.8169 | 0.8158 | 0.8158 | 0.9411 |
| No log | 4.5 | 90 | 0.1984 | 0.944 | 0.827 | 0.8125 | 0.8194 | 0.9435 |
| No log | 5.0 | 100 | 0.1982 | 0.9451 | 0.8313 | 0.8072 | 0.8184 | 0.9443 |
| No log | 5.5 | 110 | 0.1968 | 0.9456 | 0.8249 | 0.8124 | 0.8178 | 0.945 |
| No log | 6.0 | 120 | 0.2083 | 0.9432 | 0.8113 | 0.8173 | 0.8136 | 0.9429 |
| No log | 6.5 | 130 | 0.2105 | 0.9441 | 0.8355 | 0.8132 | 0.8236 | 0.9436 |
| No log | 7.0 | 140 | 0.2083 | 0.9439 | 0.8312 | 0.8207 | 0.8253 | 0.9439 |
| No log | 7.5 | 150 | 0.2145 | 0.9447 | 0.8293 | 0.8051 | 0.8161 | 0.9437 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.0
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