---
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: 0.4867
- Precision: 0.5088
- Recall: 0.5257
- F1: 0.5171
- Accuracy: 0.8997

## 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   | 0.6663          | 0.0       | 0.0    | 0.0    | 0.7639   |
| No log        | 2.0   | 100  | 0.5206          | 0.0178    | 0.0154 | 0.0165 | 0.8015   |
| No log        | 3.0   | 150  | 0.4083          | 0.0409    | 0.0617 | 0.0492 | 0.8346   |
| No log        | 4.0   | 200  | 0.3900          | 0.1300    | 0.2139 | 0.1617 | 0.8368   |
| No log        | 5.0   | 250  | 0.3372          | 0.1893    | 0.2987 | 0.2317 | 0.8598   |
| No log        | 6.0   | 300  | 0.2828          | 0.2713    | 0.3622 | 0.3102 | 0.8907   |
| No log        | 7.0   | 350  | 0.3583          | 0.3625    | 0.4066 | 0.3833 | 0.8890   |
| No log        | 8.0   | 400  | 0.2786          | 0.3638    | 0.4605 | 0.4065 | 0.8995   |
| No log        | 9.0   | 450  | 0.3000          | 0.3224    | 0.4181 | 0.3641 | 0.8981   |
| 0.3576        | 10.0  | 500  | 0.3055          | 0.4872    | 0.5145 | 0.5005 | 0.9085   |
| 0.3576        | 11.0  | 550  | 0.2949          | 0.4633    | 0.5106 | 0.4858 | 0.9123   |
| 0.3576        | 12.0  | 600  | 0.3481          | 0.4407    | 0.5723 | 0.4979 | 0.9054   |
| 0.3576        | 13.0  | 650  | 0.3636          | 0.4814    | 0.5241 | 0.5018 | 0.9054   |
| 0.3576        | 14.0  | 700  | 0.3186          | 0.4981    | 0.5010 | 0.4995 | 0.9132   |
| 0.3576        | 15.0  | 750  | 0.3472          | 0.4329    | 0.5780 | 0.4950 | 0.9084   |
| 0.3576        | 16.0  | 800  | 0.3664          | 0.4843    | 0.5665 | 0.5222 | 0.9177   |
| 0.3576        | 17.0  | 850  | 0.3666          | 0.4371    | 0.6089 | 0.5089 | 0.9085   |
| 0.3576        | 18.0  | 900  | 0.4685          | 0.4894    | 0.5356 | 0.5115 | 0.9167   |
| 0.3576        | 19.0  | 950  | 0.3722          | 0.4309    | 0.5703 | 0.4909 | 0.9154   |
| 0.0824        | 20.0  | 1000 | 0.3861          | 0.5327    | 0.5645 | 0.5482 | 0.9097   |
| 0.0824        | 21.0  | 1050 | 0.6866          | 0.5201    | 0.4239 | 0.4671 | 0.8853   |
| 0.0824        | 22.0  | 1100 | 0.5474          | 0.4616    | 0.6493 | 0.5396 | 0.8934   |
| 0.0824        | 23.0  | 1150 | 0.4203          | 0.5714    | 0.5857 | 0.5785 | 0.9168   |
| 0.0824        | 24.0  | 1200 | 0.4038          | 0.3748    | 0.5568 | 0.4481 | 0.8989   |
| 0.0824        | 25.0  | 1250 | 0.4873          | 0.5564    | 0.5414 | 0.5488 | 0.9123   |
| 0.0824        | 26.0  | 1300 | 0.4516          | 0.5306    | 0.5838 | 0.5560 | 0.9170   |
| 0.0824        | 27.0  | 1350 | 0.4349          | 0.5738    | 0.5915 | 0.5825 | 0.9110   |
| 0.0824        | 28.0  | 1400 | 0.4042          | 0.5250    | 0.5857 | 0.5537 | 0.9083   |
| 0.0824        | 29.0  | 1450 | 0.4187          | 0.6107    | 0.6166 | 0.6136 | 0.9103   |
| 0.0475        | 30.0  | 1500 | 0.3910          | 0.4615    | 0.6127 | 0.5265 | 0.9060   |
| 0.0475        | 31.0  | 1550 | 0.4171          | 0.5541    | 0.6416 | 0.5946 | 0.9133   |
| 0.0475        | 32.0  | 1600 | 0.4948          | 0.5730    | 0.6127 | 0.5922 | 0.9109   |
| 0.0475        | 33.0  | 1650 | 0.4637          | 0.5048    | 0.6089 | 0.5520 | 0.9118   |
| 0.0475        | 34.0  | 1700 | 0.3740          | 0.5431    | 0.6185 | 0.5784 | 0.9213   |
| 0.0475        | 35.0  | 1750 | 0.4047          | 0.5280    | 0.5992 | 0.5614 | 0.9129   |
| 0.0475        | 36.0  | 1800 | 0.4010          | 0.5352    | 0.6301 | 0.5788 | 0.9150   |
| 0.0475        | 37.0  | 1850 | 0.5743          | 0.5905    | 0.5530 | 0.5711 | 0.9108   |
| 0.0475        | 38.0  | 1900 | 0.4936          | 0.5110    | 0.4913 | 0.5010 | 0.9102   |
| 0.0475        | 39.0  | 1950 | 0.4450          | 0.4537    | 0.5857 | 0.5114 | 0.9119   |
| 0.0424        | 40.0  | 2000 | 0.4611          | 0.4983    | 0.5588 | 0.5268 | 0.9130   |
| 0.0424        | 41.0  | 2050 | 0.4748          | 0.5199    | 0.5279 | 0.5239 | 0.9075   |
| 0.0424        | 42.0  | 2100 | 0.5121          | 0.5264    | 0.5568 | 0.5412 | 0.9126   |


### Framework versions

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