Text Classification
Transformers
PyTorch
Portuguese
xlm-roberta
msmarco
miniLM
tensorflow
pt-br
text-embeddings-inference
Instructions to use unicamp-dl/mMiniLM-L6-v2-pt-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unicamp-dl/mMiniLM-L6-v2-pt-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="unicamp-dl/mMiniLM-L6-v2-pt-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("unicamp-dl/mMiniLM-L6-v2-pt-v2") model = AutoModelForSequenceClassification.from_pretrained("unicamp-dl/mMiniLM-L6-v2-pt-v2") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "./data/mMiniLM-L6-H384-distilled-from-XLMR-Large/", | |
| "architectures": [ | |
| "XLMRobertaForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 384, | |
| "id2label": { | |
| "0": "LABEL_0" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1536, | |
| "label2id": { | |
| "LABEL_0": 0 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 6, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "sbert_ce_default_activation_function": "torch.nn.modules.linear.Identity", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.11.3", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 250002 | |
| } | |