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
| { | |
| "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 | |
| } |