---
license: apache-2.0
base_model: DmitryPogrebnoy/MedRuRobertaLarge
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: MedRuRobertaLarge_pos
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# MedRuRobertaLarge_pos

This model is a fine-tuned version of [DmitryPogrebnoy/MedRuRobertaLarge](https://huggingface.co./DmitryPogrebnoy/MedRuRobertaLarge) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4823
- Precision: 0.4746
- Recall: 0.5274
- F1: 0.4996
- Accuracy: 0.9031

## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 50   | 0.6491          | 0.0       | 0.0    | 0.0    | 0.7598   |
| No log        | 2.0   | 100  | 0.6401          | 0.0       | 0.0    | 0.0    | 0.7664   |
| No log        | 3.0   | 150  | 0.4835          | 0.0195    | 0.0193 | 0.0194 | 0.8187   |
| No log        | 4.0   | 200  | 0.4325          | 0.0790    | 0.1368 | 0.1001 | 0.8181   |
| No log        | 5.0   | 250  | 0.3456          | 0.1653    | 0.2370 | 0.1948 | 0.8675   |
| No log        | 6.0   | 300  | 0.3438          | 0.2128    | 0.2697 | 0.2379 | 0.8744   |
| No log        | 7.0   | 350  | 0.3814          | 0.3415    | 0.2948 | 0.3164 | 0.8832   |
| No log        | 8.0   | 400  | 0.3005          | 0.3026    | 0.3854 | 0.3390 | 0.8877   |
| No log        | 9.0   | 450  | 0.2641          | 0.3718    | 0.5279 | 0.4363 | 0.8997   |
| 0.4044        | 10.0  | 500  | 0.2754          | 0.4036    | 0.5164 | 0.4531 | 0.9057   |
| 0.4044        | 11.0  | 550  | 0.3153          | 0.4041    | 0.6416 | 0.4959 | 0.8949   |
| 0.4044        | 12.0  | 600  | 0.3362          | 0.4428    | 0.5222 | 0.4792 | 0.9094   |
| 0.4044        | 13.0  | 650  | 0.3325          | 0.4433    | 0.5645 | 0.4966 | 0.9109   |
| 0.4044        | 14.0  | 700  | 0.2921          | 0.4320    | 0.5568 | 0.4865 | 0.9064   |
| 0.4044        | 15.0  | 750  | 0.3871          | 0.4630    | 0.5780 | 0.5141 | 0.9080   |
| 0.4044        | 16.0  | 800  | 0.3479          | 0.4218    | 0.6339 | 0.5065 | 0.8946   |
| 0.4044        | 17.0  | 850  | 0.3886          | 0.4914    | 0.6031 | 0.5415 | 0.9096   |
| 0.4044        | 18.0  | 900  | 0.5079          | 0.5108    | 0.5491 | 0.5292 | 0.9076   |
| 0.4044        | 19.0  | 950  | 0.3963          | 0.4344    | 0.6763 | 0.5290 | 0.8999   |
| 0.0912        | 20.0  | 1000 | 0.3845          | 0.5033    | 0.5915 | 0.5438 | 0.9145   |
| 0.0912        | 21.0  | 1050 | 0.5141          | 0.3986    | 0.4239 | 0.4108 | 0.8925   |
| 0.0912        | 22.0  | 1100 | 0.4587          | 0.4706    | 0.5395 | 0.5027 | 0.9028   |
| 0.0912        | 23.0  | 1150 | 0.4360          | 0.5017    | 0.5800 | 0.5380 | 0.9075   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2