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---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
model-index:
- name: multiclass-classifier-patents
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. -->
# multiclass-classifier-patents
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0067
- F1 Micro: 0.7001
- Precision Micro: 0.8337
- Recall Micro: 0.6034
- Exact Match F1: 0.5296
- Exact Match Precision: 0.5296
- Exact Match Recall: 0.5296
- Any Match F1: 0.9079
- Any Match Precision: 0.9079
- Any Match Recall: 0.9079
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | Precision Micro | Recall Micro | Exact Match F1 | Exact Match Precision | Exact Match Recall | Any Match F1 | Any Match Precision | Any Match Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------------:|:------------:|:--------------:|:---------------------:|:------------------:|:------------:|:-------------------:|:----------------:|
| 0.01 | 1.0 | 1292 | 0.0083 | 0.5977 | 0.8265 | 0.4681 | 0.4300 | 0.4300 | 0.4300 | 0.7675 | 0.7675 | 0.7675 |
| 0.0077 | 2.0 | 2584 | 0.0074 | 0.6595 | 0.8326 | 0.5460 | 0.4879 | 0.4879 | 0.4879 | 0.8636 | 0.8636 | 0.8636 |
| 0.007 | 3.0 | 3876 | 0.0071 | 0.6829 | 0.8173 | 0.5864 | 0.5035 | 0.5035 | 0.5035 | 0.8958 | 0.8958 | 0.8958 |
| 0.0063 | 4.0 | 5168 | 0.0069 | 0.6883 | 0.8317 | 0.5871 | 0.5140 | 0.5140 | 0.5140 | 0.8956 | 0.8956 | 0.8956 |
| 0.0058 | 5.0 | 6460 | 0.0068 | 0.6957 | 0.8337 | 0.5969 | 0.5182 | 0.5182 | 0.5182 | 0.9058 | 0.9058 | 0.9058 |
| 0.0053 | 6.0 | 7752 | 0.0069 | 0.6999 | 0.8366 | 0.6017 | 0.5271 | 0.5271 | 0.5271 | 0.9082 | 0.9082 | 0.9082 |
| 0.0048 | 7.0 | 9044 | 0.0069 | 0.7046 | 0.8159 | 0.6201 | 0.5225 | 0.5225 | 0.5225 | 0.9185 | 0.9185 | 0.9185 |
| 0.0046 | 8.0 | 10336 | 0.0069 | 0.7069 | 0.8100 | 0.6271 | 0.5241 | 0.5241 | 0.5241 | 0.9196 | 0.9196 | 0.9196 |
| 0.0042 | 9.0 | 11628 | 0.0070 | 0.7064 | 0.8208 | 0.6200 | 0.5282 | 0.5282 | 0.5282 | 0.9174 | 0.9174 | 0.9174 |
| 0.004 | 10.0 | 12920 | 0.0070 | 0.7064 | 0.8184 | 0.6214 | 0.5276 | 0.5276 | 0.5276 | 0.9177 | 0.9177 | 0.9177 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.0.1+cu117
- Datasets 3.0.1
- Tokenizers 0.20.3
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