Summarization
Transformers
PyTorch
TensorBoard
mt5
text2text-generation
Generated from Trainer
Eval Results (legacy)
Instructions to use SandraB/mt5-small-mlsum_training_sample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SandraB/mt5-small-mlsum_training_sample 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="SandraB/mt5-small-mlsum_training_sample")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("SandraB/mt5-small-mlsum_training_sample") model = AutoModelForSeq2SeqLM.from_pretrained("SandraB/mt5-small-mlsum_training_sample", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SandraB/mt5-small-mlsum_training_sample: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/SandraB/mt5-small-mlsum_training_sample/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SandraB/mt5-small-mlsum_training_sample/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SandraB/mt5-small-mlsum_training_sample/resolve/main/pytorch_model.bin
1.2 GB
- Xet hash:
- dc4f9cdd862a6d2bc1f259b5cdfac404d85c782be184ca448197fbcfa98ab9fa
- Size of remote file:
- 1.2 GB
- SHA256:
- a9a1b3af1f012869eb297d520e348cd8e9aaed44750fb4d1eee137b369f966b5
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