Instructions to use Almancy/practica_3_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Almancy/practica_3_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Almancy/practica_3_model")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Almancy/practica_3_model") model = AutoModelForQuestionAnswering.from_pretrained("Almancy/practica_3_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,879 Bytes
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library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: practica_3_model
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. -->
# practica_3_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6600
- Start Accuracy: 0.3233
- End Accuracy: 0.33
- Total Accuracy: 0.3267
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Start Accuracy | End Accuracy | Total Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:------------:|:--------------:|
| 3.6709 | 1.0 | 88 | 3.6060 | 0.1367 | 0.1133 | 0.125 |
| 3.171 | 2.0 | 176 | 3.1622 | 0.1967 | 0.15 | 0.1733 |
| 2.6294 | 3.0 | 264 | 2.8054 | 0.2983 | 0.275 | 0.2867 |
| 2.3842 | 4.0 | 352 | 2.6600 | 0.3233 | 0.33 | 0.3267 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1
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