Summarization
Transformers
PyTorch
TensorBoard
ONNX
Safetensors
German
bart
text2text-generation
Generated from Trainer
Eval Results (legacy)
Instructions to use Shahm/bart-german with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shahm/bart-german with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="Shahm/bart-german")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Shahm/bart-german") model = AutoModelForSeq2SeqLM.from_pretrained("Shahm/bart-german") - Notebooks
- Google Colab
- Kaggle
File size: 507 Bytes
5877b72 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"epoch": 3.0,
"eval_gen_len": 63.1723,
"eval_loss": 1.215167760848999,
"eval_rouge1": 41.698,
"eval_rouge2": 31.3548,
"eval_rougeL": 38.2817,
"eval_rougeLsum": 39.6349,
"eval_runtime": 3094.5108,
"eval_samples": 11394,
"eval_samples_per_second": 3.682,
"eval_steps_per_second": 0.614,
"train_loss": 0.744481670575304,
"train_runtime": 62011.6805,
"train_samples": 220887,
"train_samples_per_second": 10.686,
"train_steps_per_second": 1.781
} |