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 171
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 sha256:7f6a663e78acbef10fa9f51bf27e406e0ae66d411e8a6ae6dbbdfb20c576af34
|
| 3 |
size 1109972056
|
training_log.csv
CHANGED
|
@@ -169,3 +169,4 @@ epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second
|
|
| 169 |
168.0,0.32935049167218033,0.7143141747185975,0.15063323080539703,14.5708,176.586,5.559,0.2483482316362223,110544
|
| 170 |
169.0,0.3275724276544029,0.7137872594955638,0.15060226619243622,14.3405,179.422,5.648,0.24640497473766032,111202
|
| 171 |
170.0,0.3289668847252883,0.7144210159015004,0.15053008496761322,14.3575,179.21,5.642,0.24873688301593472,111860
|
|
|
|
|
|
| 169 |
168.0,0.32935049167218033,0.7143141747185975,0.15063323080539703,14.5708,176.586,5.559,0.2483482316362223,110544
|
| 170 |
169.0,0.3275724276544029,0.7137872594955638,0.15060226619243622,14.3405,179.422,5.648,0.24640497473766032,111202
|
| 171 |
170.0,0.3289668847252883,0.7144210159015004,0.15053008496761322,14.3575,179.21,5.642,0.24873688301593472,111860
|
| 172 |
+
171.0,0.3263113011420617,0.7141216991963261,0.15051139891147614,14.4747,177.758,5.596,0.24757092887679752,112518
|