Summarization
Transformers
Safetensors
English
bart
text2text-generation
abstractive
hybrid
multistep
Eval Results (legacy)
Instructions to use MikaSie/LexLM_BART_fixed_V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MikaSie/LexLM_BART_fixed_V1 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="MikaSie/LexLM_BART_fixed_V1")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("MikaSie/LexLM_BART_fixed_V1") model = AutoModelForSeq2SeqLM.from_pretrained("MikaSie/LexLM_BART_fixed_V1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4cc4ca87ab71f8ea7aeea891c03f03b7dd0297a3f6f57c3384cec2fae55e788c
- Size of remote file:
- 5.18 kB
- SHA256:
- 39eae1f3aecd33935f9313ca8729116e7099468aa37937dd98462f63b8deb377
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