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
Training in progress, epoch 256
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1109972056
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0d2c17405d360c90b55c3fa2f23ba532e48c93e5a29ad551a0bda9a75d5313ac
|
| 3 |
size 1109972056
|
training_log.csv
CHANGED
|
@@ -254,3 +254,4 @@ epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second
|
|
| 254 |
253.0,0.33917901406043693,0.7185974698675167,0.14871463179588318,14.4122,178.53,5.62,0.2549553050913331,166474
|
| 255 |
254.0,0.337421069715233,0.7182650218861918,0.14878371357917786,14.731,174.666,5.499,0.25068013991449667,167132
|
| 256 |
255.0,0.3377230022093698,0.7183441962286681,0.14866621792316437,14.6148,176.054,5.542,0.25301204819277107,167790
|
|
|
|
|
|
| 254 |
253.0,0.33917901406043693,0.7185974698675167,0.14871463179588318,14.4122,178.53,5.62,0.2549553050913331,166474
|
| 255 |
254.0,0.337421069715233,0.7182650218861918,0.14878371357917786,14.731,174.666,5.499,0.25068013991449667,167132
|
| 256 |
255.0,0.3377230022093698,0.7183441962286681,0.14866621792316437,14.6148,176.054,5.542,0.25301204819277107,167790
|
| 257 |
+
256.0,0.3386792730209478,0.718807453416149,0.14871138334274292,14.4282,178.332,5.614,0.2514574426739215,168448
|