Instructions to use palakagl/bert_MultiClass_TextClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use palakagl/bert_MultiClass_TextClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="palakagl/bert_MultiClass_TextClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("palakagl/bert_MultiClass_TextClassification") model = AutoModelForSequenceClassification.from_pretrained("palakagl/bert_MultiClass_TextClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from palakagl/bert_MultiClass_TextClassification: direct link, hf CLI and curl.
- Browser
- Download file 1.42 kB
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https://huggingface.co/palakagl/bert_MultiClass_TextClassification/resolve/refs%2Fpr%2F1/README.md
- Command line
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hf download hf://palakagl/bert_MultiClass_TextClassification@refs/pr/1/README.md
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curl -L -o README.md https://huggingface.co/palakagl/bert_MultiClass_TextClassification/resolve/refs%2Fpr%2F1/README.md
1.42 kB
| tags: autotrain | |
| language: en | |
| widget: | |
| - text: "I love AutoTrain 🤗" | |
| datasets: | |
| - palakagl/autotrain-data-PersonalAssitant | |
| co2_eq_emissions: 5.080390550458655 | |
| # Model Trained Using AutoTrain | |
| - Problem type: Multi-class Classification | |
| - Model ID: 717221775 | |
| - CO2 Emissions (in grams): 5.080390550458655 | |
| ## Validation Metrics | |
| - Loss: 0.35279911756515503 | |
| - Accuracy: 0.9269102990033222 | |
| - Macro F1: 0.9261839948926327 | |
| - Micro F1: 0.9269102990033222 | |
| - Weighted F1: 0.9263981751760975 | |
| - Macro Precision: 0.9273912049203341 | |
| - Micro Precision: 0.9269102990033222 | |
| - Weighted Precision: 0.9280084437800646 | |
| - Macro Recall: 0.927250645380574 | |
| - Micro Recall: 0.9269102990033222 | |
| - Weighted Recall: 0.9269102990033222 | |
| ## 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-717221775 | |
| ``` | |
| Or Python API: | |
| ``` | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| model = AutoModelForSequenceClassification.from_pretrained("palakagl/autotrain-PersonalAssitant-717221775", use_auth_token=True) | |
| tokenizer = AutoTokenizer.from_pretrained("palakagl/autotrain-PersonalAssitant-717221775", use_auth_token=True) | |
| inputs = tokenizer("I love AutoTrain", return_tensors="pt") | |
| outputs = model(**inputs) | |
| ``` |