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 tokenizer.json from SandraB/mt5-small-mlsum_training_sample: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/SandraB/mt5-small-mlsum_training_sample/resolve/main/tokenizer.json
- Command line
-
hf download hf://SandraB/mt5-small-mlsum_training_sample/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/SandraB/mt5-small-mlsum_training_sample/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- bc4fa692ed53150c52b49bd726f093559062cd2fe84f7d5d01412429bfb1d22d
- Size of remote file:
- 16.3 MB
- SHA256:
- 62601b29fdb680d1001627ce0b3e6a90e451c59bd440d80bc57b826489c7958f
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