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---
base_model: KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align
library_name: transformers
metrics:
- accuracy
- precision
- recall
- f1
tags:
- generated_from_trainer
model-index:
- name: dfm
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. -->
# Dialogue_dfm
This model is a fine-tuned version of [KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align](https://huggingface.co/KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align) on the None dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9417
- Precision: 0.9468
- Recall: 0.9417
- F1: 0.9418
- Loss: 0.4894
## 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: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Accuracy | Precision | Recall | F1 | Validation Loss |
|:-------------:|:-------:|:----:|:--------:|:---------:|:------:|:------:|:---------------:|
| No log | 0.9412 | 8 | 0.7223 | 0.7770 | 0.7223 | 0.7069 | 0.8079 |
| No log | 2.0 | 17 | 0.7821 | 0.8280 | 0.7821 | 0.7670 | 0.7157 |
| No log | 2.9412 | 25 | 0.9217 | 0.9243 | 0.9217 | 0.9174 | 0.3617 |
| No log | 4.0 | 34 | 0.9283 | 0.9331 | 0.9283 | 0.9272 | 0.3444 |
| No log | 4.9412 | 42 | 0.9156 | 0.9274 | 0.9156 | 0.9168 | 0.4618 |
| No log | 6.0 | 51 | 0.9271 | 0.9316 | 0.9271 | 0.9277 | 0.3164 |
| No log | 6.9412 | 59 | 0.9356 | 0.9387 | 0.9356 | 0.9349 | 0.3228 |
| No log | 8.0 | 68 | 0.9329 | 0.9398 | 0.9329 | 0.9334 | 0.4814 |
| No log | 8.9412 | 76 | 0.9402 | 0.9450 | 0.9402 | 0.9400 | 0.4819 |
| No log | 10.0 | 85 | 0.9409 | 0.9459 | 0.9409 | 0.9409 | 0.4952 |
| No log | 10.9412 | 93 | 0.9367 | 0.9428 | 0.9367 | 0.9370 | 0.5182 |
| No log | 12.0 | 102 | 0.9409 | 0.9462 | 0.9409 | 0.9411 | 0.4947 |
| No log | 12.9412 | 110 | 0.9405 | 0.9457 | 0.9405 | 0.9406 | 0.4927 |
| No log | 14.0 | 119 | 0.9409 | 0.9462 | 0.9409 | 0.9411 | 0.4912 |
| No log | 14.9412 | 127 | 0.9413 | 0.9465 | 0.9413 | 0.9414 | 0.4917 |
| No log | 16.0 | 136 | 0.9413 | 0.9464 | 0.9413 | 0.9415 | 0.4893 |
| No log | 16.9412 | 144 | 0.9413 | 0.9464 | 0.9413 | 0.9415 | 0.4890 |
| No log | 18.0 | 153 | 0.9417 | 0.9468 | 0.9417 | 0.9418 | 0.4893 |
| No log | 18.8235 | 160 | 0.9417 | 0.9468 | 0.9417 | 0.9418 | 0.4894 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1
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