Text Classification
Transformers
Safetensors
English
bert
NLP
BERT
FinBERT
FinTwitBERT
sentiment
finance
financial-analysis
sentiment-analysis
financial-sentiment-analysis
twitter
tweets
tweet-analysis
stocks
stock-market
crypto
cryptocurrency
text-embeddings-inference
Instructions to use StephanAkkerman/FinTwitBERT-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StephanAkkerman/FinTwitBERT-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="StephanAkkerman/FinTwitBERT-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("StephanAkkerman/FinTwitBERT-sentiment") model = AutoModelForSequenceClassification.from_pretrained("StephanAkkerman/FinTwitBERT-sentiment") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 857 Bytes
b07ab8b 4696827 b07ab8b | 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 33 34 35 36 37 | {
"_name_or_path": "output/FinTwitBERT-tweeteval",
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "NEUTRAL",
"1": "BULLISH",
"2": "BEARISH"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"BEARISH": 2,
"BULLISH": 1,
"NEUTRAL": 0
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"torch_dtype": "float32",
"transformers_version": "4.35.0",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30875
}
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