# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("DanielAvelar09/mi_chatbotV2")
model = AutoModelForQuestionAnswering.from_pretrained("DanielAvelar09/mi_chatbotV2", device_map="auto")Quick Links
mi_chatbotV2
This model is a fine-tuned version of timpal0l/mdeberta-v3-base-squad2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.7635
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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 66 | 1.4198 |
| No log | 2.0 | 132 | 1.8198 |
| No log | 3.0 | 198 | 1.7635 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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Model tree for DanielAvelar09/mi_chatbotV2
Base model
microsoft/mdeberta-v3-base Finetuned
timpal0l/mdeberta-v3-base-squad2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="DanielAvelar09/mi_chatbotV2")