Text Classification
Transformers
Safetensors
xlm-roberta
sentiment-analysis
thai
multilingual
fine-tuned
southeast-asian
text-embeddings-inference
Instructions to use ZombitX64/MultiSent-E5-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZombitX64/MultiSent-E5-Pro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ZombitX64/MultiSent-E5-Pro")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ZombitX64/MultiSent-E5-Pro") model = AutoModelForSequenceClassification.from_pretrained("ZombitX64/MultiSent-E5-Pro", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +10 -8
config.json
CHANGED
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@@ -4,24 +4,26 @@
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"classifier_dropout":
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"eos_token_id": 2,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"id2label": {
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"0": "
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"1": "
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"2": "
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"3": "
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},
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"label2id": {
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"
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"negative": 1,
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"neutral": 2,
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"positive": 3
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},
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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}
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"classifier_dropout": 0.1,
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"eos_token_id": 2,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"id2label": {
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"0": "very negative",
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"1": "negative",
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"2": "neutral",
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"3": "positive",
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"4": "very positive"
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},
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"label2id": {
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"very negative": 0,
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"negative": 1,
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"neutral": 2,
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"positive": 3,
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"very positive": 4
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},
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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}
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