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 45
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 sha256:37e617739c8d79eff90597c5832a51587c53fd147629c1aee819929b4f58f9ee
|
| 3 |
size 1109972056
|
training_log.csv
CHANGED
|
@@ -43,3 +43,4 @@ epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second
|
|
| 43 |
42.0,0.26285499213340974,0.6872115680443032,0.1609029471874237,14.6913,175.138,5.513,0.2153128643606685,27636
|
| 44 |
43.0,0.2645249221714536,0.687557603686636,0.16080811619758606,14.2372,180.723,5.689,0.21764477263894286,28294
|
| 45 |
44.0,0.26446413709855177,0.6879098360655738,0.16047069430351257,14.4894,177.578,5.59,0.21764477263894286,28952
|
|
|
|
|
|
| 43 |
42.0,0.26285499213340974,0.6872115680443032,0.1609029471874237,14.6913,175.138,5.513,0.2153128643606685,27636
|
| 44 |
43.0,0.2645249221714536,0.687557603686636,0.16080811619758606,14.2372,180.723,5.689,0.21764477263894286,28294
|
| 45 |
44.0,0.26446413709855177,0.6879098360655738,0.16047069430351257,14.4894,177.578,5.59,0.21764477263894286,28952
|
| 46 |
+
45.0,0.2655991083531059,0.688909389093891,0.16015583276748657,14.3258,179.606,5.654,0.21842207539836767,29610
|