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 377
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:f1a381c4a4675c31cb2c002ece1160e546945d3e436104816f0833c63b395f88
|
| 3 |
size 1109972056
|
training_log.csv
CHANGED
|
@@ -375,3 +375,4 @@ epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second
|
|
| 375 |
374.0,0.345733135754941,0.7215982506709074,0.14748044312000275,14.3641,179.127,5.639,0.25767586474931986,246092
|
| 376 |
375.0,0.34573428308683213,0.7216013510157453,0.1474667340517044,14.544,176.912,5.569,0.25806451612903225,246750
|
| 377 |
376.0,0.3448946400901754,0.7214044161527751,0.14744730293750763,14.5143,177.274,5.581,0.2568985619898951,247408
|
|
|
|
|
|
| 375 |
374.0,0.345733135754941,0.7215982506709074,0.14748044312000275,14.3641,179.127,5.639,0.25767586474931986,246092
|
| 376 |
375.0,0.34573428308683213,0.7216013510157453,0.1474667340517044,14.544,176.912,5.569,0.25806451612903225,246750
|
| 377 |
376.0,0.3448946400901754,0.7214044161527751,0.14744730293750763,14.5143,177.274,5.581,0.2568985619898951,247408
|
| 378 |
+
377.0,0.34497865028006286,0.7213962508080155,0.14744699001312256,14.5538,176.792,5.566,0.25728721336960747,248066
|