Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Loys/assets")
model = AutoModelForSequenceClassification.from_pretrained("Loys/assets")Quick Links
assets
This model is a fine-tuned version of iHealthGroup/shc-lm-v1 on the shc_deva_ds dataset. It achieves the following results on the evaluation set:
- Loss: 0.7051
- F1: 0.3333
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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 1.1628 | 0.62 | 400 | 0.7051 | 0.3333 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.13.0+cpu
- Datasets 2.4.0
- Tokenizers 0.11.6
- Downloads last month
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Space using Loys/assets 1
Evaluation results
- F1 on shc_deva_dsself-reported0.333
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Loys/assets")