Instructions to use xshubhamx/google-t5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xshubhamx/google-t5-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xshubhamx/google-t5-small")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xshubhamx/google-t5-small") model = AutoModelForSequenceClassification.from_pretrained("xshubhamx/google-t5-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "google-t5/t5-small", | |
| "architectures": [ | |
| "T5ForSequenceClassification" | |
| ], | |
| "classifier_dropout": 0.0, | |
| "d_ff": 2048, | |
| "d_kv": 64, | |
| "d_model": 512, | |
| "decoder_start_token_id": 0, | |
| "dense_act_fn": "relu", | |
| "dropout_rate": 0.1, | |
| "eos_token_id": 1, | |
| "feed_forward_proj": "relu", | |
| "id2label": { | |
| "0": "Issue", | |
| "1": "Court Discourse", | |
| "2": "Conclusion", | |
| "3": "Precedent Analysis", | |
| "4": "Section Analysis", | |
| "5": "Argument by Petitioner", | |
| "6": "Fact", | |
| "7": "Argument by Respondent", | |
| "8": "Ratio", | |
| "9": "Appellant", | |
| "10": "Respondent", | |
| "11": "Argument by Appellant", | |
| "12": "Petitioner", | |
| "13": "Judge", | |
| "14": "Argument by Defendant" | |
| }, | |
| "initializer_factor": 1.0, | |
| "is_encoder_decoder": true, | |
| "is_gated_act": false, | |
| "label2id": { | |
| "Appellant": 9, | |
| "Argument by Appellant": 11, | |
| "Argument by Defendant": 14, | |
| "Argument by Petitioner": 5, | |
| "Argument by Respondent": 7, | |
| "Conclusion": 2, | |
| "Court Discourse": 1, | |
| "Fact": 6, | |
| "Issue": 0, | |
| "Judge": 13, | |
| "Petitioner": 12, | |
| "Precedent Analysis": 3, | |
| "Ratio": 8, | |
| "Respondent": 10, | |
| "Section Analysis": 4 | |
| }, | |
| "layer_norm_epsilon": 1e-06, | |
| "model_type": "t5", | |
| "n_positions": 512, | |
| "num_decoder_layers": 6, | |
| "num_heads": 8, | |
| "num_layers": 6, | |
| "output_past": true, | |
| "pad_token_id": 0, | |
| "problem_type": "single_label_classification", | |
| "relative_attention_max_distance": 128, | |
| "relative_attention_num_buckets": 32, | |
| "task_specific_params": { | |
| "summarization": { | |
| "early_stopping": true, | |
| "length_penalty": 2.0, | |
| "max_length": 200, | |
| "min_length": 30, | |
| "no_repeat_ngram_size": 3, | |
| "num_beams": 4, | |
| "prefix": "summarize: " | |
| }, | |
| "translation_en_to_de": { | |
| "early_stopping": true, | |
| "max_length": 300, | |
| "num_beams": 4, | |
| "prefix": "translate English to German: " | |
| }, | |
| "translation_en_to_fr": { | |
| "early_stopping": true, | |
| "max_length": 300, | |
| "num_beams": 4, | |
| "prefix": "translate English to French: " | |
| }, | |
| "translation_en_to_ro": { | |
| "early_stopping": true, | |
| "max_length": 300, | |
| "num_beams": 4, | |
| "prefix": "translate English to Romanian: " | |
| } | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.38.2", | |
| "use_cache": true, | |
| "vocab_size": 32128 | |
| } | |