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 315
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:a0252b410e7b63c9a65085fc230cc593fa4d0826d5695d23b5d52e7b5441700f
|
| 3 |
size 1109972056
|
training_log.csv
CHANGED
|
@@ -313,3 +313,4 @@ epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second
|
|
| 313 |
312.0,0.3428882313942803,0.7203697261839686,0.14800423383712769,14.4871,177.607,5.591,0.2545666537116207,205296
|
| 314 |
313.0,0.34347583466469334,0.7209094519459889,0.14802765846252441,14.5061,177.374,5.584,0.2549553050913331,205954
|
| 315 |
314.0,0.3419251985993704,0.7208550832711906,0.14799219369888306,14.637,175.787,5.534,0.2565099106101827,206612
|
|
|
|
|
|
| 313 |
312.0,0.3428882313942803,0.7203697261839686,0.14800423383712769,14.4871,177.607,5.591,0.2545666537116207,205296
|
| 314 |
313.0,0.34347583466469334,0.7209094519459889,0.14802765846252441,14.5061,177.374,5.584,0.2549553050913331,205954
|
| 315 |
314.0,0.3419251985993704,0.7208550832711906,0.14799219369888306,14.637,175.787,5.534,0.2565099106101827,206612
|
| 316 |
+
315.0,0.3426776380245612,0.7202185792349727,0.14798329770565033,14.4338,178.263,5.612,0.25534395647104546,207270
|