Instructions to use NLPScholars/Roberta-Earning-Call-Transcript-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NLPScholars/Roberta-Earning-Call-Transcript-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NLPScholars/Roberta-Earning-Call-Transcript-Classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NLPScholars/Roberta-Earning-Call-Transcript-Classification") model = AutoModelForSequenceClassification.from_pretrained("NLPScholars/Roberta-Earning-Call-Transcript-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from NLPScholars/Roberta-Earning-Call-Transcript-Classification: direct link, hf CLI and curl.
- Browser
- Download file 991 Bytes
-
https://huggingface.co/NLPScholars/Roberta-Earning-Call-Transcript-Classification/resolve/ad768dcf0f113e71a7cc29957cdda70c6065d8d7/config.json
- Command line
-
hf download hf://NLPScholars/Roberta-Earning-Call-Transcript-Classification@ad768dcf0f113e71a7cc29957cdda70c6065d8d7/config.json
-
curl -L -o config.json https://huggingface.co/NLPScholars/Roberta-Earning-Call-Transcript-Classification/resolve/ad768dcf0f113e71a7cc29957cdda70c6065d8d7/config.json
991 Bytes
| { | |
| "_name_or_path": "roberta-base", | |
| "architectures": [ | |
| "RobertaForSequenceClassification" | |
| ], | |
| "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": 768, | |
| "id2label": { | |
| "0": "Positive", | |
| "1": "Negative", | |
| "2": "Litigious", | |
| "3": "Constraining", | |
| "4": "Uncertainty" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "Positive": 0, | |
| "Negative": 1, | |
| "Litigious": 2, | |
| "Constraining": 3, | |
| "Uncertainty": 4 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "problem_type": "multi_label_classification", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.11.0", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 50265 | |
| } | |