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 327
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 sha256:36e08e2b220f23edb7aa32bdde19770aa4f7b76fb5160a1efe48635c88a1141c
|
| 3 |
size 1109972056
|
training_log.csv
CHANGED
|
@@ -325,3 +325,4 @@ epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second
|
|
| 325 |
324.0,0.34415262753759757,0.7207985697258641,0.14787223935127258,14.3617,179.157,5.64,0.25728721336960747,213192
|
| 326 |
325.0,0.3416958739418116,0.72036640613332,0.14789409935474396,14.497,177.485,5.587,0.25806451612903225,213850
|
| 327 |
326.0,0.341807310123352,0.7204900886542485,0.14783377945423126,14.5363,177.005,5.572,0.2565099106101827,214508
|
|
|
|
|
|
| 325 |
324.0,0.34415262753759757,0.7207985697258641,0.14787223935127258,14.3617,179.157,5.64,0.25728721336960747,213192
|
| 326 |
325.0,0.3416958739418116,0.72036640613332,0.14789409935474396,14.497,177.485,5.587,0.25806451612903225,213850
|
| 327 |
326.0,0.341807310123352,0.7204900886542485,0.14783377945423126,14.5363,177.005,5.572,0.2565099106101827,214508
|
| 328 |
+
327.0,0.34345725452344894,0.7209221443831669,0.14785908162593842,14.3682,179.076,5.637,0.25806451612903225,215166
|