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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 205
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 sha256:14d3d1a8cc4cf3683b2efd6c3a66e92e8de91ef332aa9a0c642f083ebf077616
|
| 3 |
size 1109972056
|
training_log.csv
CHANGED
|
@@ -203,3 +203,4 @@ epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second
|
|
| 203 |
202.0,0.33227022996650596,0.7163226642736066,0.14968156814575195,14.342,179.404,5.648,0.25068013991449667,132916
|
| 204 |
203.0,0.33411590221525117,0.7168833756787725,0.149742990732193,14.2706,180.301,5.676,0.2510687912942091,133574
|
| 205 |
204.0,0.3337613878808395,0.7165056280506027,0.14963068068027496,14.4288,178.324,5.614,0.2495141857753595,134232
|
|
|
|
|
|
| 203 |
202.0,0.33227022996650596,0.7163226642736066,0.14968156814575195,14.342,179.404,5.648,0.25068013991449667,132916
|
| 204 |
203.0,0.33411590221525117,0.7168833756787725,0.149742990732193,14.2706,180.301,5.676,0.2510687912942091,133574
|
| 205 |
204.0,0.3337613878808395,0.7165056280506027,0.14963068068027496,14.4288,178.324,5.614,0.2495141857753595,134232
|
| 206 |
+
205.0,0.33307549448180607,0.716282320055905,0.14959056675434113,14.5571,176.752,5.564,0.2518460940536339,134890
|