Instructions to use palakagl/Roberta_Multiclass_TextClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use palakagl/Roberta_Multiclass_TextClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="palakagl/Roberta_Multiclass_TextClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("palakagl/Roberta_Multiclass_TextClassification") model = AutoModelForSequenceClassification.from_pretrained("palakagl/Roberta_Multiclass_TextClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from palakagl/Roberta_Multiclass_TextClassification: direct link, hf CLI and curl.
- Browser
- Download file 1.43 kB
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https://huggingface.co/palakagl/Roberta_Multiclass_TextClassification/resolve/refs%2Fpr%2F1/README.md
- Command line
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hf download hf://palakagl/Roberta_Multiclass_TextClassification@refs/pr/1/README.md
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curl -L -o README.md https://huggingface.co/palakagl/Roberta_Multiclass_TextClassification/resolve/refs%2Fpr%2F1/README.md
1.43 kB
| tags: autotrain | |
| language: en | |
| widget: | |
| - text: "I love AutoTrain 🤗" | |
| datasets: | |
| - palakagl/autotrain-data-PersonalAssitant | |
| co2_eq_emissions: 0.014567637985425905 | |
| # Model Trained Using AutoTrain | |
| - Problem type: Multi-class Classification | |
| - Model ID: 717221783 | |
| - CO2 Emissions (in grams): 0.014567637985425905 | |
| ## Validation Metrics | |
| - Loss: 0.38848456740379333 | |
| - Accuracy: 0.9180509413067552 | |
| - Macro F1: 0.9157418163085091 | |
| - Micro F1: 0.9180509413067552 | |
| - Weighted F1: 0.9185290137253468 | |
| - Macro Precision: 0.9189981206383326 | |
| - Micro Precision: 0.9180509413067552 | |
| - Weighted Precision: 0.9221607328493303 | |
| - Macro Recall: 0.9158232837734661 | |
| - Micro Recall: 0.9180509413067552 | |
| - Weighted Recall: 0.9180509413067552 | |
| ## Usage | |
| You can use cURL to access this model: | |
| ``` | |
| $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/palakagl/autotrain-PersonalAssitant-717221783 | |
| ``` | |
| Or Python API: | |
| ``` | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| model = AutoModelForSequenceClassification.from_pretrained("palakagl/autotrain-PersonalAssitant-717221783", use_auth_token=True) | |
| tokenizer = AutoTokenizer.from_pretrained("palakagl/autotrain-PersonalAssitant-717221783", use_auth_token=True) | |
| inputs = tokenizer("I love AutoTrain", return_tensors="pt") | |
| outputs = model(**inputs) | |
| ``` |