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t5
text2text-generation
seq2seq
text-generation-inference
Instructions to use yhavinga/t5-base-36L-ccmatrix-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yhavinga/t5-base-36L-ccmatrix-multi with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" 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("translation", model="yhavinga/t5-base-36L-ccmatrix-multi")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("yhavinga/t5-base-36L-ccmatrix-multi") model = AutoModelForSeq2SeqLM.from_pretrained("yhavinga/t5-base-36L-ccmatrix-multi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Save weights at end of training
Browse files- events.out.tfevents.1650667398.t1v-n-ab5b8a40-w-0.78521.0.v2 +2 -2
- flax_model.msgpack +1 -1
- pytorch_model.bin +1 -1
- test_results.json +10 -0
events.out.tfevents.1650667398.t1v-n-ab5b8a40-w-0.78521.0.v2
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pytorch_model.bin
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size 2915983933
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test_results.json
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{
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"test_bp": 0.9413807776309435,
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"test_precision_ng1": 76.01892285298399,
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"test_precision_ng2": 54.01958080529509,
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"test_precision_ng3": 41.72523961661342,
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"test_precision_ng4": 32.91607396870555,
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"test_ref_len": 17484,
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"test_score": 45.87597143301609,
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"test_sys_len": 16488
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}
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