Text Classification
Transformers
Safetensors
Spanish
new
xlm-roberta
multilingual
social-media
custom_code
text-embeddings-inference
Instructions to use SimoneAstarita/es-no-bio-20251013-t01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SimoneAstarita/es-no-bio-20251013-t01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SimoneAstarita/es-no-bio-20251013-t01", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("SimoneAstarita/es-no-bio-20251013-t01", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 4,223 Bytes
983ee73 | 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 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | {
"overall": {
"at_0.5": {
"precision_macro": 0.6608465608465608,
"recall_macro": 0.7035714285714285,
"f1_macro": 0.6764388665555447,
"precision_weighted": 0.8374859708193041,
"recall_weighted": 0.8106060606060606,
"f1_weighted": 0.821655123645515,
"accuracy": 0.8106060606060606,
"confusion_matrix": [
[
96,
16
],
[
9,
11
]
],
"classification_report": " precision recall f1-score support\n\n no-recl (0) 0.9143 0.8571 0.8848 112\n recl (1) 0.4074 0.5500 0.4681 20\n\n accuracy 0.8106 132\n macro avg 0.6608 0.7036 0.6764 132\nweighted avg 0.8375 0.8106 0.8217 132\n",
"threshold": 0.5
},
"at_best_global": {
"precision_macro": 0.6608465608465608,
"recall_macro": 0.7035714285714285,
"f1_macro": 0.6764388665555447,
"precision_weighted": 0.8374859708193041,
"recall_weighted": 0.8106060606060606,
"f1_weighted": 0.821655123645515,
"accuracy": 0.8106060606060606,
"confusion_matrix": [
[
96,
16
],
[
9,
11
]
],
"classification_report": " precision recall f1-score support\n\n no-recl (0) 0.9143 0.8571 0.8848 112\n recl (1) 0.4074 0.5500 0.4681 20\n\n accuracy 0.8106 132\n macro avg 0.6608 0.7036 0.6764 132\nweighted avg 0.8375 0.8106 0.8217 132\n",
"threshold": 0.5
},
"at_best_by_lang": {
"precision_macro": 0.6608465608465608,
"recall_macro": 0.7035714285714285,
"f1_macro": 0.6764388665555447,
"precision_weighted": 0.8374859708193041,
"recall_weighted": 0.8106060606060606,
"f1_weighted": 0.821655123645515,
"accuracy": 0.8106060606060606,
"confusion_matrix": [
[
96,
16
],
[
9,
11
]
],
"classification_report": " precision recall f1-score support\n\n no-recl (0) 0.9143 0.8571 0.8848 112\n recl (1) 0.4074 0.5500 0.4681 20\n\n accuracy 0.8106 132\n macro avg 0.6608 0.7036 0.6764 132\nweighted avg 0.8375 0.8106 0.8217 132\n",
"thresholds_by_lang": {
"es": 0.5
}
}
},
"thresholds": {
"global_best": {
"threshold": 0.5,
"f1_macro": 0.6764388665555447,
"precision_macro": 0.6608465608465608,
"recall_macro": 0.7035714285714285
},
"by_lang_best": {
"es": {
"threshold": 0.5,
"f1_macro": 0.6764388665555447,
"precision_macro": 0.6608465608465608,
"recall_macro": 0.7035714285714285
}
},
"default": 0.5
},
"per_lang": {
"at_0.5": [
{
"lang": "es",
"n": 132,
"accuracy": 0.8106060606060606,
"f1_macro": 0.6764388665555447,
"precision_macro": 0.6608465608465608,
"recall_macro": 0.7035714285714285,
"f1_weighted": 0.821655123645515,
"precision_weighted": 0.8374859708193041,
"recall_weighted": 0.8106060606060606
}
],
"at_best_global": [
{
"lang": "es",
"n": 132,
"accuracy": 0.8106060606060606,
"f1_macro": 0.6764388665555447,
"precision_macro": 0.6608465608465608,
"recall_macro": 0.7035714285714285,
"f1_weighted": 0.821655123645515,
"precision_weighted": 0.8374859708193041,
"recall_weighted": 0.8106060606060606
}
],
"at_best_by_lang": [
{
"lang": "es",
"n": 132,
"accuracy": 0.8106060606060606,
"f1_macro": 0.6764388665555447,
"precision_macro": 0.6608465608465608,
"recall_macro": 0.7035714285714285,
"f1_weighted": 0.821655123645515,
"precision_weighted": 0.8374859708193041,
"recall_weighted": 0.8106060606060606
}
]
}
} |