Instructions to use microsoft/ssr-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/ssr-base 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="microsoft/ssr-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("microsoft/ssr-base") model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/ssr-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 682 Bytes
00ea093 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | {
"_name_or_path": "ssr-base",
"architectures": [
"T5ForConditionalGeneration"
],
"d_ff": 3072,
"d_kv": 64,
"d_model": 768,
"decoder_start_token_id": 0,
"dropout_rate": 0.1,
"early_stopping": true,
"eos_token_id": 1,
"initializer_factor": 1.0,
"is_encoder_decoder": true,
"layer_norm_epsilon": 1e-06,
"length_penalty": 2.0,
"max_length": 200,
"min_length": 30,
"model_type": "t5",
"n_positions": 512,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"num_decoder_layers": 12,
"num_heads": 12,
"num_layers": 12,
"output_past": true,
"pad_token_id": 0,
"relative_attention_num_buckets": 32,
"save_step": 34,
"vocab_size": 32128
}
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