Text Classification
Transformers
Safetensors
English
multilingual
xlm-roberta
multi-label-classification
multi-head-classification
disaster-response
humanitarian-aid
social-media
twitter
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use spencercdz/xlm-roberta-sentiment-requests with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spencercdz/xlm-roberta-sentiment-requests with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="spencercdz/xlm-roberta-sentiment-requests")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("spencercdz/xlm-roberta-sentiment-requests") model = AutoModel.from_pretrained("spencercdz/xlm-roberta-sentiment-requests") - Notebooks
- Google Colab
- Kaggle
| epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second,eval_steps_per_second,eval_subset_accuracy,step | |
| 1.0,0.07219894765752088,0.4952816012502254,0.272717148065567,15.8417,162.42,5.113,0.10532452390205985,658 | |
| 2.0,0.09064480010203674,0.544629877304181,0.22911879420280457,14.4231,178.394,5.616,0.11232024873688301,1316 | |
| 3.0,0.10309514614135397,0.5682428955343358,0.21433667838573456,16.6207,154.807,4.873,0.12786630392537893,1974 | |
| 4.0,0.115960350269004,0.5877596855699045,0.20582592487335205,14.3765,178.973,5.634,0.1333074232413525,2632 | |
| 5.0,0.12550720862659767,0.6022160664819944,0.1996934562921524,14.2268,180.855,5.693,0.1379712397979013,3290 | |
| 6.0,0.13109005516624317,0.6116456809809204,0.19493445754051208,14.4708,177.806,5.597,0.14380101049358726,3948 | |
| 7.0,0.1371925209709484,0.6188629785374912,0.19109617173671722,14.6334,175.831,5.535,0.14380101049358726,4606 | |
| 8.0,0.14152951052414994,0.6225964149918523,0.18794220685958862,14.3092,179.814,5.661,0.14457831325301204,5264 | |
| 9.0,0.14935181006664486,0.6283051582141309,0.18516190350055695,14.5423,176.932,5.57,0.15001943256898562,5922 | |
| 10.0,0.15952444743230132,0.635693929128354,0.18292535841464996,14.5123,177.298,5.581,0.15235134084726001,6580 | |
| 11.0,0.16772819936516337,0.6397169204374866,0.18077120184898376,14.1317,182.073,5.732,0.15934706568208318,7238 | |
| 12.0,0.17159071497969608,0.6419475655430712,0.17905373871326447,14.4557,177.992,5.603,0.16090167120093277,7896 | |
| 13.0,0.1789171582067714,0.6459455136748947,0.1774977147579193,14.4174,178.465,5.618,0.16556548775748153,8554 | |
| 14.0,0.18644449382556874,0.6495780926604044,0.17618677020072937,14.3979,178.707,5.626,0.16750874465604354,9212 | |
| 15.0,0.19204255281350513,0.6531367263886687,0.1748652458190918,14.451,178.05,5.605,0.16673144189661873,9870 | |
| 16.0,0.19736083782585614,0.6549719961957096,0.17370301485061646,14.1778,181.481,5.713,0.1737271667314419,10528 | |
| 17.0,0.20190792482249356,0.6579917816879148,0.17270566523075104,14.2818,180.16,5.672,0.17839098328799066,11186 | |
| 18.0,0.2055350851972657,0.6601992658626115,0.1718790978193283,14.4913,177.555,5.59,0.1748931208705791,11844 | |
| 19.0,0.20890000572492995,0.6614753235820268,0.17088289558887482,14.3311,179.54,5.652,0.18655266226195102,12502 | |
| 20.0,0.21269686074800662,0.6629672285624542,0.17023713886737823,14.4262,178.357,5.615,0.18227749708511465,13160 | |
| 21.0,0.2173037125153878,0.6656912948061449,0.16946576535701752,14.278,180.208,5.673,0.18771861640108822,13818 | |
| 22.0,0.22153695778755486,0.6674666110183639,0.1687048375606537,14.5098,177.328,5.582,0.190439176059075,14476 | |
| 23.0,0.2228445020395818,0.6689608425882476,0.16809335350990295,14.4792,177.703,5.594,0.1916051301982122,15134 | |
| 24.0,0.22818936455009958,0.6706499714241181,0.1676853448152542,14.4154,178.49,5.619,0.190439176059075,15792 | |